System and method for detecting inhibition of a biological assay

ABSTRACT

In some examples, a system for detecting inhibition of a biological assay includes a detection device configured to amplify and detect a target nucleic acid. The detection device is configured to receive a sample comprising a matrix and a quantity of the target nucleic acid and to amplify the target nucleic acid within the sample over a nucleic acid amplification cycle. The detection device is configured to capture a data set including measurements of the nucleic acid collected during the amplification cycle. The system further includes a computing device configured to receive the data set and to apply a machine-learning system to the data set to detect inhibited biological assays that tested negative for the target nucleic acid due to matrix inhibition.

TECHNICAL FIELD

This disclosure relates to systems and methods for detecting inhibitionof biological assays, and, in particular, to systems and methods fordetecting whether a biological assay is inhibited.

BACKGROUND

Foodborne bacterial infections and diseases are an ongoing threat topublic health. Regulatory agencies such as the United States Departmentof Agriculture's Food Safety and Inspection Service respond to thisthreat by promulgating pathogen-reduction performance standards forpathogens (e.g., Salmonella and Campylobacter) in food, feed, water, andcorresponding processing environments.

Food, feed, and water producers use quantitative and/or qualitativetechniques to determine the quantity and/or presence of microorganisms,such as bacterial pathogens, in food, feed (e.g., animal feed), waterand in corresponding processing environments. Such producers may, forinstance, perform quantitation of total and indicator bacteria to assessthe effectiveness of pathogen-intervention processes such as hazardanalysis and critical control points (HACCP)-based food safetyprocedures and other hygiene control measures. These same producers mayperform threshold tests for target organisms at other points of theprocess, issuing an indication of whether a sample tested positive ornegative for the target organism.

Typically, people seeking to determine the quantity of a pathogen relyon traditional methods of quantitation, such as most probable number(MPN) estimates based on serial culture dilution. Such approaches areoften time consuming, tedious, and error-prone. Biological assays suchas DNA/RNA amplification, using reporters such as bioluminescence orfluorescence, on the other hand, are used to determine if a sampletested positive or negative for the target organism.

SUMMARY

The disclosure provides systems for detecting inhibition of a biologicalassay (e.g., a particular sample of food, feed, water, or correspondingenvironmental sample) being evaluated to detect the presence of and/orto quantify one or more target organisms using nucleic acidamplification assays. The disclosure also provides systems and methodsfor training a machine learning system to detect inhibited biologicalassays and to issue a result indicating whether the biological assay isinhibited.

An example system for detecting inhibition of a biological assayincludes a detection device configured to amplify and detect a targetnucleic acid associated with a target organism during the biologicalassay, the detection device comprising a reaction chamber configured toreceive a sample comprising a matrix and a quantity of the targetnucleic acid and to amplify the target nucleic acid within the sampleover a nucleic acid amplification cycle; and a detector, the detectorconfigured to capture, during the nucleic acid amplification cycle,measurements representative of a quantity of the target nucleic acidpresent in the sample and to store the measurements in a data set. Thedetection device further including a machine learning system configuredto receive the data set, wherein the machine-learning system includesprocessing circuitry trained to detect biological assays inhibited dueto matrix inhibition.

In one example, a method includes receiving a plurality of data sets,wherein each data set is associated with a biological assay, each dataset including measurements, performed on the associated biological assayby a nucleic acid amplification device of a specified type and collectedover at least a portion of a nucleic acid amplification cycle, of atarget nucleic acid detected within the associated biological assay,wherein the target nucleic acid is associated with a target organism.The method further includes labeling, as false negative data sets, thosedata sets from the plurality of data sets that are associated withbiological assays that tested negative for the target nucleic acid dueto matrix inhibition and that would have tested positive had matrixinhibition not been present and labeling, as true negative data sets,those data sets from the plurality of data sets that are associated withbiological assays that correctly tested negative for the target nucleicacid. The method further includes training a machine-learning systemswith the true negative and false negative data sets to detect biologicalassays that tested negative for the target nucleic acid due to matrixinhibition.

An example non-transitory computer-readable medium storing instructionsthat, when executed by processing circuitry, cause the processingcircuitry to receive a data set generated by amplifying and detecting atarget nucleic acid associated with a target organism in a samplecomprising a matrix and a quantity of the target nucleic acid over anucleic acid amplification cycle, the data set including measurementsrepresentative of the quantity of the target nucleic acid present in thesample and to store the measurements in a data set, wherein the data setincludes; and apply a machine-learning system to the data set. Themachine-learning technique is trained to detect inhibited biologicalassays and to issue a result indicating whether the biological assaytested negative for the target nucleic acid due to matrix inhibition.

An example non-transitory computer-readable medium stores instructionsthat, when executed by processing circuitry, cause the processingcircuitry to establish a machine-learning system trained to detectmatrix-inhibited biological assays; receive a data set generated byamplifying and detecting a target nucleic acid associated with a targetorganism in a sample comprising a matrix and a quantity of the targetnucleic acid over a nucleic acid amplification cycle, the data setincluding measurements representative of the quantity of the targetnucleic acid present in the sample; determine, by applying themachine-learning system to the data set, whether the data set is from amatrix-inhibited sample; and label the data set accordingly.

Thus, in the systems and methods described herein, the data resultingfrom a biological assay may be collected and analyzed using machinelearning systems, such as support vector machines, boosted decisiontrees, systems, and/or others. Such data may be used to train and buildmachine learning systems for particular pathogens and/or matrices. Themachine learning systems, trained with one or more proper data sets, canexamine much or all of a signal response in molecular diagnostic assays(e.g., qPCR and/or LAMP). Such machine-learning systems, trained withthe proper data set, can examine a background response of a nucleicacid-based molecular diagnostic assay and compute the probability thatthe background signal corresponds to a matrix that is rendering theassay unable to produce a positive reaction (i.e., is inhibited).Enabling the identification of false-negative results may help improveeffectiveness of pathogen-intervention processes used during foodproduction relative to molecular methods that do not include theapplication of such trained machine-learning systems.

The details of one or more examples are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages of the disclosure will be apparent from the description anddrawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating an example system that includes anucleic acid amplification device configured to amplify and detect atarget nucleic acid and a user device configured to receive data fromthe nucleic acid amplification device and to apply a machine-learningsystem to the data, in accordance with aspects of the disclosure.

FIG. 2 is a block diagram illustrating an example system that includesan external device, such as a server, and an access point coupled to thenucleic acid amplification device of FIG. 1 via a network, in accordancewith one aspect of this disclosure.

FIG. 3 is a schematic and conceptual diagram illustrating the exampleuser device of FIG. 1, in accordance with one aspect of the disclosure.

FIG. 4 is a flow diagram illustrating example points for pathogentesting before, during, and/or after food, feed, or water production, inaccordance with one aspect of the disclosure.

FIG. 5A is a flow diagram illustrating an example technique forestimating a quantity of the target organism in a sample, in accordancewith one aspect of the disclosure.

FIG. 5B is a flow diagram illustrating an example technique fordetecting inhibition of a biological assay, in accordance with oneaspect of the disclosure.

FIG. 6 illustrates real-time detection of nucleic acid amplificationduring a LAMP amplification cycle based on measurements ofbioluminescence intensity over time, in accordance with one aspect ofthis disclosure.

FIG. 7 is a schematic drawing illustrating representative features of anexample qPCR technique, in accordance with one aspect of thisdisclosure.

FIG. 8A is a flow diagram illustrating an example approach for traininga machine learning system, in accordance with aspects of thisdisclosure.

FIG. 8B is a flow diagram illustrating an example approach that uses thetrained machine learning system of FIG. 8A to estimate a quantity of atarget organism, in accordance with aspects of this disclosure.

FIG. 9 illustrates a test results from an example pathogen detectionsystem indicative of an assay error, an inhibited but valid assay, and avalid assay, in accordance with one aspect of this disclosure.

FIGS. 10A and 10B are flow diagrams illustrating example techniques forusing data sets that tested negative for a target organism to train amachine-learning system to detect inhibited biological assays.

FIG. 10C is a flow diagram illustrating an example technique for usingthe trained machine learning system to detect inhibited biologicalassays, in accordance with one aspect of this disclosure.

FIG. 11 is a block diagram illustrating a device training system, inaccordance with one aspect of this disclosure.

FIG. 12 illustrates an analysis report for environmental samplesproduced by an example pathogen detection system, in accordance with oneaspect of this disclosure.

FIG. 13 illustrates a workflow depicting an application of a matrixcontrol and dilution to the samples shown in the analysis report of FIG.12, in accordance with one aspect of this disclosure.

FIG. 14 illustrates results of an application of a trained two-classdecision forest algorithm to a collection of data sets, in accordancewith one aspect of this disclosure.

FIG. 15 illustrates results of an application of a trained two-classdecision forest algorithm to training data set, in accordance with oneaspect of this disclosure.

FIG. 16 illustrates an example of a collection of data sets, whereineach data set is represented by a curve representing light intensityover time during one or more nucleic acid amplification cycles, eachcurve corresponding to a sample, in accordance with one aspect of thisdisclosure.

FIG. 17 illustrates results of an application of the trainedmachine-learning technique of FIGS. 10A-10C (e.g., trained according tothe example technique of FIG. 10A) to the data sets of FIG. 16, inaccordance with one aspect of this disclosure.

FIG. 18 illustrates an analysis report produced by an example pathogendetection system for a collection of data sets taken from samplesassociated with matrix ingredients known to be inhibitory, in accordancewith one aspect of this disclosure.

FIG. 19 illustrates results of an application of a trainedmachine-learning system to the data set of FIG. 18, in accordance withone aspect of this disclosure.

FIG. 20 illustrates an example application of a trained machine-learningsystem to data sets of quality assurance (QA) laboratory negativecontrol runs, in accordance with one aspect of this disclosure.

FIG. 21 illustrates an example of a collection of data sets, whereineach data set is represented by a curve representing light intensityover time during one or more nucleic acid amplification cycles, eachcurve corresponding to a sample known to include an inhibitory matrixresulting in a false negative result.

FIG. 22 illustrates an example application of a trained machine-learningsystem (e.g., trained according to the example technique of FIG. 10C) tothe data sets of FIG. 21, in accordance with one aspect of thisdisclosure.

FIGS. 23-25 illustrate application of the machine learning system todata sets from samples containing 6, 7 dihydroxycoumarin, in accordancewith one aspect of this disclosure.

DETAILED DESCRIPTION

Molecular methods are increasingly used to detect the presence of andquantity of target organisms in a sample. Assays based on molecularmethods such as nucleic acid amplification ((e.g., LAMP or PCR) arehighly efficient. They can, however, be affected by the presence ofmatrix-derived substances which can interfere or prevent the reactionfrom performing correctly, a process termed inhibition. In foodproduction, matrix-derived substances, such as spices and environmentalsamples, may act as inhibitors that can interfere with nucleotideamplification assays such as PCR and LAMP, leading to false negativeresults.

It can be difficult to eliminate inhibition. Careful sample treatmentmay be used, for instance, to remove inhibitory substances. No sampletreatment, however, can be relied on to completely remove inhibitorysubstances.

Amplification controls may also be used to control for inhibition. Suchcontrols may be used, for instance, to verify that the assay hasperformed correctly. Typically, an internal amplification control (IAC)is a non-target DNA sequence present in the very same reaction as thesample or target nucleic acid extract. If it is successfully amplifiedto produce a signal, any non-production of a target signal in thereaction is considered to signify that the sample did not contain thetarget pathogen or organism. If, however, the reaction produces neithera signal from the target nor the IAC, it signifies that the reaction hasfailed, signally the absence of the target organism when, in fact, thetarget organism is present (i.e., a “false negative”). Detection offalse negatives during the amplification cycle may be, therefore,critical for reliable testing.

The addition of amplification controls adds complexity and cost tomolecular methods. It would be advantageous to eliminate the use ofamplification controls when applying molecular methods to detect orquantify target organisms in a sample, even in the face of inhibition.Approaches for detecting false negatives in inhibited samples are,therefore, presented below. These approaches may, for instance, be usedto detect false negatives in nucleotide amplification without the needfor internal or external amplification controls.

In the following discussion, the term “food” also includes beverages.The term “water” includes drinking water, but the term “water” alsoincludes water used in other situations that require detection of orquantitative measurement of one or more of the microorganisms in thewater.

As noted above, food, feed and water producers use quantitative and/orqualitative techniques to determine the quantity and/or presence ofmicroorganisms, such as bacterial pathogens, in food, feed (e.g., animalfeed), water and corresponding processing environments. Quantitativetechniques are used, for instance, to assess the effectiveness ofpathogen-intervention processes used during food production. Suchanalysis may lead to more effective risk analyses and to the developmentof more effective ways to reduce the level of pathogens in the food,feed and water supply.

Molecular methods (e.g., LAMP or PCR) may be used to detect the presenceof and quantity of target organisms in a sample. These methods areroutinely used for detecting presence/absence of pathogens (qualitative)and offer faster results (1 day) than traditional culture-based methods(3-5 days). Such methods may also be used to quantitate pathogensextracted from a sample, as discussed below. Molecular methods ofpathogen quantification provide results more quickly than moretraditional methods (e.g., in hours rather than one or more days). Theyalso are not limited to quantification of total bacteria and indicatorbacteria, but also may be used to quantify specific bacteria, yeast,mold, or other pathogens.

An advantage of molecular methods is that amplification occurs at apredictable rate given appropriate conditions. For instance, qPCR iswidely used as a molecular method for detecting a variety of bacteria.qPCR may also be used for the absolute quantification of pathogenspresent in a given amount of sample. Standard curves containing knownamounts of the target DNA (plasmids, genomic DNAs or other nucleic acidmolecules) are run in parallel with the unknown samples. Based on thestandard curve, the efficiency of the reaction and the dilution stepsused for the nucleic acid extraction and analysis, the absolute numberof pathogens in the unknown samples may be estimated. In these types ofanalysis, linear regression models are used, the efficiency ofamplification becomes critical and standards need to be run with everyrun, adding to cost, time, possible contamination of samples.Furthermore, the standard curve approach has limited use when cellcounts (not DNA) are being used. For these reasons, traditional methodsof determining the quantity of a pathogen in a sample remain in use.

However, to improve detection sensitivity, it may be advantageous tohave single target rather than multianalyte detection, since reagentscan compete leading to incomplete amplification of targets and henceinaccurate results. Some of the newly developed techniques such as LAMPare robust, use simple detection technologies allowing more ease of useand simpler instruments.

Pathogen detection as discussed below includes both qualitative andquantitative detection. The following disclosure further describessystems and methods for training and using machine learning systems inmolecular methods of pathogen detection, thereby improving the accuracyof pathogen detection and for quantification assays, reducing oreliminating the need for preparing and using standard curves with everyrun. In some example methods described herein, LAMP bioluminescentassays and/or PCR assays (e.g., qPCR assays) may be used in a trainingrun to amplify a target nucleic acid (e.g., a nucleic acid associatedwith a target organism) present in a sample in a known initial quantityand to detect light generated within the sample during amplification ofthe target nucleic acid. In other example methods described herein,assays such as nicking-enzyme amplification reaction (NEAR),helicase-dependent amplification (HDA), nucleic acid sequence-basedamplification (NASBA), or transcription-mediated amplification (TMA)assays may be used in a training run to amplify a target nucleic acid(e.g., a nucleic acid associated with a target organism) present in asample in a known initial quantity and to provide measurementscorresponding to amplification of the known initial quantity of thetarget organism.

Any suitable variation on such assays may be used. Variations on atraditional LAMP assay that may be used may include colorimetric LAMP(cLAMP) assays, in which pH changes driven by the accumulation ofprotons during LAMP can be visualized via observation of color changesof a pH-sensitive colorimetric dye that occur with nucleic acidamplification. Other such variations may include turbidity-LAMP assays,in which formation of magnesium pyrophosphate during LAMP results inturbidity that increases in correlation with nucleic acid yield and thatcan be quantified in real-time. Materials and methods used in suchvariations on traditional LAMP assays, and/or on PCR assays, may beunderstood by those of skill in the art and thus are not described indetail here. It should be understood that example nucleic acidamplification techniques and variations thereon described herein are notintended to be limiting. Instead, any suitable nucleic acidamplification technique may be used in the techniques described herein,such as in a training run to amplify a target nucleic acid.

Data from the training run may be fed into a machine learning system totrain the machine learning system. The trained machine learning systemthen may be used to detect presence/absence of target organism and/orestimate an unknown initial quantity of the target organism present in asample, such as a food sample, feed sample, water or environmentalsample from a food or feed processing environment.

In example methods described herein, LAMP bioluminescent assays and/orPCR assays (e.g., qPCR assays) may be used in a training run to amplifya target nucleic acid (e.g., a nucleic acid associated with a targetorganism) present in a series of samples with known inhibitors/matricesof nucleic acid amplification assays, determine inhibition usingdilution of the samples and/or use of an internal or externalamplification control. The method collects data for each samplerepresentative of light generated within the sample during amplificationof the target nucleic acid and associates the collected data withpresence/absence of target organisms and/or known quantities of thetarget nucleic acid, or with known quantities of the organism beingdetected.

In other example methods described within, LAMP bioluminescent assaysand/or PCR assays (e.g., qPCR assays) may be used in a training run toamplify a target nucleic acid (e.g., a nucleic acid associated with atarget organism) present in a series of samples having known initialquantities of the target organism. The method collects data for eachsample representative of light generated within the sample duringamplification of the target nucleic acid and associates the collecteddata with known quantities of the target nucleic acid, or with knownquantities of the organism being detected.

Data from the training run is then fed into a machine learning system totrain the machine learning system. The trained machine learning systemmay then be used to determine inhibition or not of the target organismpresent in a sample, such as a food sample, feed sample, water orenvironmental sample from a food or feed processing environment.

In yet other example methods described within, LAMP bioluminescentassays and/or PCR assays (e.g., qPCR assays) may be used to obtain datacorresponding to samples collected from a particular environment (e.g.,a poultry processing plant or a cheese factory). The samples arereviewed using traditional presence/absence and/or quantitation methodsand each sample is labeled with the value determined via one or more ofthe traditional methods. The data from the labeled samples is then fedinto a machine learning system to train the machine learning system forthat particular environment. The trained machine learning system maythen be used to better determine presence/absence and/or estimate anunknown initial quantity of the target organism and/or nucleic acidpresent in a sample, such as a food sample, feed sample, water orenvironmental sample from the particular environment.

It should be noted that while in some examples nucleic acids associatedwith a target organism may be described herein as being DNA, in otherexamples, a nucleic acid associated with a target organism may be anRNA. In such other examples, an amplification technique such asquantitative reverse transcription PCR (RT-qPCR) and reversetranscription LAMP (RT-LAMP) on total RNA or mRNA of a sample may beused in a method of training a machine learning system to estimate aninitial quantity of a target organism in a sample and/or in applyingsuch a trained machine learning system.

Each machine learning system is based on at least one model. The modelmay be a regression model based on techniques such as, for example,support vector regression, random forest regression, linear regression,ridge regression, logistic regression, Lasso, or nearest neighborregression. Or the model may be a classification model based ontechniques such as, for example, support vector machines, decision treeand random forest, linear discriminant analysis, neural networks,nearest neighbor classifier, stochastic gradient descent classifier,gaussian process classification, or naïve bayes. Both types of modelsrely on the use of labeled data sets to train the model.

Samples from food, feed, water and corresponding processing environmentsmay include a matrix that includes material, such as carbohydrates,lipids, proteins, pigments, spices, and/or other components of the food,feed, water or environment from which the sample was obtained. Some suchmatrices inhibit or prevent the amplification of a target nucleic acid,such as by preventing the polymerase from extending the nucleic acid inthe time allowed, thereby producing incomplete amplification productsand hindering accurate detection and/or quantification of the targetorganism. This problem is termed “matrix inhibition.”

In samples having a matrix that includes one or more inhibitors, nucleicacid amplification and detection assays may provide a false-negativeresult even though the sample does include an amount of the targetorganism that should have resulted in detection of the target organism.Although dilution of a sample may help alleviate the inhibitory effectof a matrix that includes one or more inhibitors, dilution may alsocause loss of signal associated with the target nucleic acid if theinitial quantity of the nucleic acid preset in the sample was relativelylow.

The following disclosure, therefore, also describes systems and methodsfor detecting matrix inhibition of biological assays and systems andmethods for training and using machine learning systems to detect matrixinhibition of biological assays. The described systems and methodsimprove the accuracy of pathogen detection and quantification (e.g., byreducing or eliminating the occurrence of false-negative results of suchbiological assays).

In some examples, the systems and methods for training and usingmachine-learning systems to detect matrix inhibition of biologicalassays include systems and methods that distinguish between biologicalassays that correctly test negative for the target nucleic acid (i.e.,“true negative” biological assays) and biological assays that testednegative for the target nucleic acid due to matrix inhibition but thatwould have tested positive had matrix inhibition not been present (i.e.,“false negative” biological assays). Such an approach may reduce oreliminate the need for the use of internal and/or external controls withnucleic acid amplification and detection methods to detect suchinhibition. In some such example approaches, experts review biologicalassays identified during the nucleic acid amplification cycle asnegative for the target organism, labeling each biological assay aseither a true negative or a false negative. Measurements recorded duringthe nucleic acid amplification cycle for each biological assay are thenused to train a machine learning system to distinguish betweenbiological assays that are truly negative and those that would testpositive but for matrix inhibition.

As noted above, matrices in food samples, environmental samples, blood,fecal samples, may include inhibitors that prevent theamplification/binding of the analyte. Often an internal control isemployed to monitor the inhibition. In assays using a single reportermolecule and assays that do not enable multiplex capability, an externalcontrol may be used to monitor such inhibition. Biological assays suchas DNA/RNA amplification, immunoassays, using reporters such asbioluminescence, fluorescence, or colorimetry, usually have an intrinsicbackground. The intrinsic background may be used to calibrate theassays. The background or baseline portions of the reporter signal innucleic acid-based molecular diagnostic assays may be, for instance, theproduct of unbound fluorescent probes, free dye, probe cleavage, matrixinterference with the signal, instrument calibration and other factors.As such, these background or baseline signals are often subtracted fromor otherwise suppressed in the reporter signal provided to a user duringnucleic acid amplification assays because they contain littleinformation relevant to the detection or quantification of the targetorganism. In some examples, however, systems and methods for trainingmachine-learning systems use the intrinsic background and/or baselineportions of the reporter signal to detect matrix inhibition.

Examples are provided below for LAMP-bioluminescent assays.LAMP-bioluminescent assays use bioluminescence at a single wavelengthand do not allow multiplex capability. However, the assays have inherentbioluminescence background, and, in some examples, this background isutilized to train a machine learning system to predict matrix inhibitionof the assays. Such an approach provides a mechanism for detectingwhether the matrix has an inhibitory effect and, therefore, forpreventing false negative outcomes.

Thus, systems and methods described herein may utilize an otherwiseunused inherent background or baseline portion of a reporter signal totrain and use a machine-learning system to distinguish inhibitedbiological assays from non-inhibited biological assays in nucleic acidamplification and detection techniques with which internal controls forinhibition may not be usable. For example, one or more data sets fromone or more corresponding training runs may be fed into amachine-learning system to train the machine-learning system. In someexamples, it may be sufficient to include in such training runs samplesfor which a false-negative result was determined and samples for which anegative result correctly was determined. It may not be necessary totrain the machine-learning system with samples for which a positiveresult was determined, which may simplify a method of training themachine-learning system. In some such examples, the machine-learningsystem may be trained to detect biological assays that tested negativefor the target nucleic acid due to matrix inhibition and that would havetested positive for the target nucleic acid had matrix inhibition notbeen present.

The trained machine-learning system then may be used to detect inhibitedbiological assays and to issue a result indicating whether thebiological assay tested negative for the target nucleic acid due tomatrix inhibition. In contrast with systems and methods that rely uponinternal or external controls for detection of inhibition, the examplesystems and methods described herein may address the issue of inhibitiondetection by using an otherwise unused inherent background or baselineportion of a reporter signal, which may reduce cost and/or time neededfor pathogen detection or quantification and/or increase throughput ofsuch assays.

FIG. 1 is a block diagram illustrating an example system that includes anucleic acid amplification device configured to amplify and detect atarget nucleic acid associated with a target organism and a user deviceconfigured to receive data from the nucleic acid amplification deviceand to apply a machine-learning system to the data, in accordance withaspects of the disclosure. Nucleic acid amplification device 8 isconfigured to amplify and detect a target nucleic acid, in accordancewith one aspect of the disclosure. Nucleic acid amplification device 8includes a reaction chamber 10 configured to amplify the target nucleicacid. In one example approach, as shown in FIG. 1, reaction chamber 10includes a block 12 that may be heated and/or cooled via a heat sourcesuch as a Peltier system. As illustrated in FIG. 1, block 12 defines aplurality of wells 14, each of which may be dimensioned to receive areaction vessel, which may be any suitable plastic tube configured foruse in nucleic acid amplification assays. Nucleic acid amplificationdevice 8 further includes a detector 16 and a control unit 18. Detector16 may be configured to capture light within reaction chamber 10 undercontrol of control unit 18. For example, detector 16 may be configuredto capture a data set including time-series measurement samples of lightemitted by a light-emitting species within sample contained within areaction vessel received within one of wells 14 during one or morenucleic acid amplification cycles. In some examples, the sample mayinclude a target nucleic acid and the light-emitting species, the latterof which may emit light in a stoichiometric relationship with the targetnucleic acid such that the light emitted by the light-emitting speciesincreases with an increase in the quantity of replicated target nucleicacid in the sample.

In some examples, nucleic acid amplification device 8 may be anysuitable nucleic acid amplification device configured for LAMP (e.g.,traditional LAMP assays, or cLAMP, turbidity LAMP, or other variationson traditional LAMP assays). In examples in which light is emitted by alight-emitting species captured by detector 16, the light may bebioluminescence, fluorescence or light of any visible color. In examplesin which a turbidity LAMP technique is used, the detector may measure atleast one of absorbance, transmittance, or reflectance. Additionally, oralternatively, nucleic acid amplification device 8 may be any suitablenucleic acid amplification device configured for qPCR or any othernucleic acid amplification technique (e.g., NEAR, HDA, NASBA, TMA, orothers). In some such other examples, light emitted by thelight-emitting species and captured by detector 16 may be fluorescence.

In some of the example methods described herein for trainingmachine-learning systems, nucleic acid amplification device 8 may be anucleic acid amplification device of a specified type. For example,nucleic acid amplification device 8 may include one or more specificfeatures and/or may be a specific model of a nucleic acid amplificationdevice from a specified manufacturer. In some such examples, a trainedmachine learning system resulting from such methods may be tailored tothe specified type of nucleic acid amplification device, which mayenhance the accuracy of the trained machine learning system. Nucleicacid amplification devices having any suitable configuration may beused. For example, a nucleic acid amplification device may include arack (e.g., a spinning rack) configured to receive reaction vesselsinstead of a block. In some such examples, the reaction vessels may becapillaries or more traditionally-configured tubes. In some examples, adetector 16 of a nucleic acid amplification device may be position abovethe reaction vessels or in any suitable position. Thus, theconfiguration of nucleic acid amplification device described herein isnot intended to be limiting but to illustrate an example.

The example system of FIG. 1 further includes user device 20, which mayinclude a processor 23 and a memory 22 used to store parametersrepresenting one or more trained machine learning systems 25. In oneexample approach, user device 20 receives a data set from control unit18 for each sample tested. In some such example approaches, each dataset includes data representing measurements of a reporter signalcaptured by a device 8 during the amplification cycle of the givensample. In some example approaches, the measurements includemeasurements of a quantity of light received by detector 16 at specifictimes during the amplification cycle of the given sample. As furtherdiscussed below with respect to FIG. 3, user device 20 may be a devicesuch as a computer workstation, tablet, or other such user deviceco-located with nucleic acid amplification device 8 in a user'slaboratory. Nucleic acid amplification device 8 may be configured totransmit the data set from control unit 18 to user device 20, such asvia any suitable wired connection (e.g., metal traces, fiber optics,Ethernet, or the like), a wireless connection (e.g., personal areanetwork, local area network, metropolitan area network, wide areanetwork, a cloud-based system, or the like), or a combination of both.For example, user device 20 may include a communications unit thatincludes a network interface card, such as an Ethernet card, an opticaltransceiver, a radio frequency transceiver, a Bluetooth® interface card,WiFi™ radios, USB, or any other type of device that can send and receiveinformation to and from nucleic acid amplification device 8.

In some example approaches, processor 23 may be configured to apply atrained machine-learning system 25 stored in memory 22 to the data setto detect inhibited biological assays and to issue a result indicatingwhether the biological assay tested negative for the target nucleic aciddue to matrix inhibition. Additionally, or alternatively, processor 23may be configured to apply a trained machine-learning system (i.e.,trained learning system 25 or another trained machine-learning systemstored in memory 22) to a data set to estimate a quantity of a targetorganism present in the biological assay as a function of the data set.In some examples, processor 23 may store the result indicating whetherthe biological assay tested negative for the target nucleic acid due tomatrix inhibition and/or the estimated quantity of the target organism,such as in association with other data pertaining to the biologicalassay. In examples in which processor 23 is configured to apply atrained machine-learning system to a data set to estimate a quantity ofthe target organism, the estimated quantity may be compared to acorresponding threshold value in a limit test to determine whether thesample passes or fails the limit test. The threshold value may, in somesuch example approaches, be a value associated with one or moreregulatory standards, industry practices, or associated interventionprocesses. For example, the estimated quantity of the target organism ina sample may help enable evaluation of effectiveness of interventionprocedures designed to improve process efficiency and/or reduce pathogenlevels in food products, feed products, water and/or correspondingpreparation environments.

In examples in which a processor (e.g., processor 23) is configured toapply a trained machine-learning system to a data set associated with anamplified sample of a target nucleic acid to estimate a quantity of thetarget organism in the sample may help address public health issuesassociated with pathogens. For example, since the systems and methodsfor nucleic acid quantitation described herein provide quantity valuesmore quickly than traditional approaches to pathogen quantitation, suchsystems and methods may make pathogen quantitation more accessible tothe food industry. This increased accessibility may be used by the foodindustry, for instance, to obtain a more nuanced understanding ofpathogen presence than can be obtained simply by detecting the presenceor absence of the pathogen. The increased accessibility may also be usedto support limit testing in pathogen analysis, as one goal of limittesting is to detect foodborne pathogen concentrations that meet orexceed a threshold concentration and limit the release of products thatmay negatively impact public health.

In this manner, the systems and methods described herein that includeapplying a trained machine learning system to a data set associated withan amplified sample of a target nucleic acid to detect and/or quantifyinhibited biological assays and to issue a result indicating whether thebiological assay tested negative for the target nucleic acid due tomatrix inhibition may help address public health issues associated withpathogens. Detection of false-negative results (e.g., results thatincorrectly indicate a target nucleic acid is not present or not presentat a threshold level) of biological assays inhibited due to matrixinhibition as described herein may help protect consumer health, such asby limiting the consumer exposure to potentially contaminated products.

FIG. 2 is a block diagram illustrating an example system 6 that includesan external device 28, such as a server, and an access point 24 coupledto the nucleic acid amplification device 8 of FIG. 1 via a network 26,in accordance with one aspect of this disclosure. In one example, asshown in FIG. 2, system 6 may include an access point 24, a network 26,and one or more external devices, such as an external device 28 (e.g., aserver), which may include a memory 32 and/or processing circuitry 30.In the example shown in FIG. 2, nucleic amplification device 8 may usecommunication circuitry (not shown) used to communicate with accesspoint 24 via a wireless connection. Access point 24 then conveys theinformation received from nucleic amplification device 8 to externaldevice 28 through network 26 via a wired connection and conveys theinformation received from external device 28 through network 26 tonucleic amplification device 8 via the wireless connection.

Access point 24 may comprise a processor that connects to network 26 viaany of a variety of connections, such as telephone dial-up, digitalsubscriber line (DSL), or cable modem, or other suitable connections. Inother examples, access point 24 may be coupled to network 26 throughdifferent forms of connections, including wired or wireless connections.In some examples, access point 24 may be a user device, such as acomputer workstation or tablet that may be co-located with nucleicamplification device 8 and the user. Nucleic amplification device 8 maybe configured to transmit data to access point 24, such as data setsdescribed above with respect to FIG. 1. In addition, access point 24 mayinterrogate nucleic amplification device 8, such as periodically or inresponse to a command from a user or from network 26, in order toretrieve data sets pertaining to one or more biological assays, or toretrieve other information stored in a memory (not shown) of nucleicamplification device 8. Access point 24 may then communicate theretrieved data to external device 28 via network 26.

In some examples, memory 32 of external device 28 may be configured toprovide a secure storage site for data collected from access point 24and/or nucleic acid amplification device 8. In some examples, memory 32stores parameters representing one or more trained machine learningsystems 35. In some examples, external device 28 may assemble the datain web pages or other documents for viewing by users via access point 24or one or more other computing devices of the system of FIG. 2. In thismanner, the system of FIG. 2 may enable remote (e.g., cloud-based)storage and access of data associated with a user's testing of food orfeed products and/or of corresponding production environments. Suchsystems may be customized to meet a particular user's data storageand/or access needs.

In some examples, memory 32 of external device 28 may be configured toprovide a secure storage site for data collected from access point 24and/or nucleic acid amplification device 8. In some examples, externaldevice 28 may assemble data in web pages or other documents for viewingby users via access point 24 or one or more other computing devices ofthe system of FIG. 2. In this manner, the system of FIG. 2 may enableremote (e.g., cloud-based) storage and access of data associated with auser's testing of food or feed products and/or of correspondingproduction environments. Such systems may be customized to meet aparticular user's data storage and/or access needs.

FIG. 3 is a schematic and conceptual diagram illustrating features ofuser device 20 of FIG. 1, in accordance with one aspect of thedisclosure. Although FIG. 3 is described with respect to user device 20of FIG. 1, one or more components of user device 20 described herein maybe functionally and/or structurally similar to one or more components ofaccess point 24 and/or external device 28 illustrated in FIG. 2. In oneexample approach, user device 20 includes user interface 40 andcomputing device 42. User interface 40 may include display 38, agraphical user interface (GUI), a keyboard, a touchscreen, a speaker, amicrophone, or the like.

One or more processors 23 of computing device 42 are configured toimplement functionality, process instructions, or both for executionwithin computing device 42. For example, processors 23 may be capable ofprocessing instructions stored within memory 22, such as instructionsfor applying a trained machine-learning system to a data set to detectinhibited biological assays and to issue a result indicating whether thebiological assay tested negative for the target nucleic acid due tomatrix inhibition and/or apply a trained machine-learning system to adata set to estimate an initial quantity of a target nucleic acid or atarget organism present in a sample. Examples of one or more processors23 may include, any one or more of a microprocessor, a controller, adigital signal processor (DSP), an application specific integratedcircuit (ASIC), a field-programmable gate array (FPGA), or equivalentdiscrete or integrated logic circuitry.

In some examples, computing device 42 may utilize one or morecommunications units 48 to communicate with one or more external devices(e.g., external device 28 of FIG. 2 and/or nucleic acid amplificationdevice 8) via one or more networks, such as one or more wired orwireless networks. Communications units 48 may include a networkinterface card, such as an Ethernet card, an optical transceiver, aradio frequency transceiver, or any other type of device configured tosend and receive information. Communications units 48 may also includeWiFi™ radios or a Universal Serial Bus (USB) interface.

In some examples, one or more output devices 50 of computing device 42may be configured to provide output to a user using, for example, audio,video or tactile media. For example, output devices 50 may includedisplay 38 of user interface 40, a sound card, a video graphics adaptercard, or any other type of device for converting a signal into anappropriate form understandable to humans or machines, such as a signalassociated with information pertaining to a status, outcome, or otheraspect of one or more data sets resulting from amplification cyclescarried out by nucleic acid amplification device 8 analyzed by a trainedmachine learning system. In some example approaches, user interface 40includes one or more of output devices 50 employed by computing device42.

Memory 22 of computing device 42 may be configured to store informationwithin computing device 42 during operation. In some examples, memory 22may include a computer-readable storage medium or computer-readablestorage device. Memory 22 may include a temporary memory, meaning that aprimary purpose of one or more components of memory 22 may notnecessarily be long-term storage. Memory 22 may include a volatilememory, meaning memory 22 does not maintain stored contents when poweris not provided thereto. Examples of volatile memories include randomaccess memories (RAM), dynamic random-access memories (DRAM), staticrandom-access memories (SRAM), and other forms of volatile memoriesknown in the art. In some examples, memory 22 may be used to storeprogram instructions for execution by processors 23, such asinstructions for applying a trained machine-learning system to a dataset received from nucleic acid amplification device 8 via one or morecommunications units 48. Memory 22 may, in some examples, be used bysoftware or applications running on computing device 42 to temporarilystore information during program execution.

In some examples, memory 22 may further include a signal processingmodule 52, a training module 54, and a detecting module 56. In some suchexamples, detecting module 56 includes a machine learning system (suchas machine learning systems 25 of FIGS. 1 and 35 of FIG. 2) that, whentrained and applied to a data set, detects inhibited biological assaysand issues a result indicating whether the biological assay testednegative for a target nucleic acid due to matrix inhibition and/orestimates the concentration of target organisms in a sample. In one suchexample approach, training module 54 receives data sets of assays withknown cell concentrations collected by a nucleic acid amplificationdevice 8 over one or more amplification cycles and uses the data sets totrain detecting module 56 to estimate the concentration of targetorganisms in a sample.

In some examples, memory 22 may include non-volatile storage elements.Examples of such non-volatile storage elements include magnetic harddiscs, optical discs, floppy discs, flash memories, or forms ofelectrically programmable memories (EPROM) or electrically erasable andprogrammable (EEPROM) memories. In one such example approach, signalprocessing module 52 may be configured to analyze data received fromnucleic acid amplification device 8, such as a data set captured bydetector 16 and comprising time-series measurement samples of the lightemitted by light-emitting species within a sample during anamplification cycle, and process the data to improve the quality of thesensor data.

Computing device 42 may also include additional components that, forclarity, are not shown in FIG. 3. For example, computing device 42 mayinclude a power supply to provide power to the components of computingdevice 42. Similarly, the components of computing device 42 shown inFIG. 3 may not be necessary in every example of computing device 42.

FIG. 4 is a flow diagram illustrating example points for pathogentesting before, during, and/or after food or feed production, inaccordance with one aspect of the disclosure. As illustrated in FIG. 4,food production environment 60 may include raw material 62. Foodproduction processes 64 that process raw material 62 and produce endproduct 66 may take place within food production environment 60. In someexamples, production processes 64 may take place entirely within foodproduction environment 60, whereas raw material 62 may enter foodproduction environment 60 from outside of food production environment 60at the beginning of the processes illustrated in FIG. 4. In someexamples, food production environment 60 may be an environment in whichfood or feed materials are harvested, such as a greenhouse or field inwhich such materials are grown. In some examples, samples from foodproduction environment 60 may be water samples from water sources withinthe food production environment 60, such as sources of water used forwashing and/or cooking.

Raw material 62 may acquire pathogens from outside food productionenvironment 60 and introduce such pathogens into food productionenvironment 60 as or after raw material 62 is introduced into foodproduction environment 60. Thus, to help reduce foodborne illness causedby pathogens, there is an increased trend in pathogen testing of rawmaterials (e.g., raw material 62) and food production environments(e.g., food production environment 60). Moreover, pathogen testing ofraw material 62 may help prevent pathogen contamination of end product66 (or of other end products) by identifying contamination before rawmaterial enters food production environment 60 such that entrance ofcontaminated raw materials into food production environment 60 may beavoided.

End product 66 may be located within environment 60 for a period of timeprior to shipment out of environment 60, such as before, during, andafter packaging. End product 66 may acquire pathogens from foodproduction environment 60, such as pathogens introduced by raw material62 or from other sources within food production environment 60. However,as discussed above, traditional methods of pathogen detection and/orquantification may be significantly time consuming, taking one or moredays to yield results, and molecular methods of pathogen detectionand/or quantification have not yet gained widespread use. In someinstances, the time required for traditional methods may limit foodprocessing rates. Moreover, due to the time requirement, suchtraditional methods provide pathogen assessment only as current as thetime the sample was taken, which may not provide an accurate assessmentof a current state of a material, environment, or product. Thus, atleast due to the time advantage of the molecular methods for pathogendetection, quantification, and or inhibition detection described herein,pathogen testing of raw material 62, food production environment, and/orend product 66 (e.g., as part of a release test), such as at test points68, according to such methods that may provide more up-to-dateassessments, which ultimately may help prevent the release ofcontaminated end products to the public.

FIG. 5A is a flow diagram illustrating an example technique forestimating a quantity of the target organism in a sample, in accordancewith one aspect of the disclosure. The example approach of FIG. 5A maybe carried out using a nucleic acid amplification device such as nucleicacid amplification device 8 of the systems of FIGS. 1 and 2. Asdescribed above with respect to FIG. 1, nucleic acid amplificationdevice 8 may be a nucleic acid amplification device of any suitable typeand may be configured to carry out any suitable nucleic acidamplification technique, such as LAMP or PCR. Although described in thecontext of the systems of FIG. 1, the example technique of FIG. 5A maybe carried out using any suitable nucleic acid amplification device andcomputing device. Systems and methods for estimating a quantity of atarget organism in a sample are described in further detail in MACHINELEARNING QUANTIFICATION OF TARGET ORGANISMS USING NUCLEIC ACIDAMPLIFICATION ASSAYS, filed herewith, the description of which isincorporated herein by reference.

In the example approach of FIG. 5A, nucleic acid amplification device 8amplifies a target nucleic acid within an enriched sample withinreaction chamber 10 (70). In some examples, the sample may be derivedfrom food production environment 60, raw material 62 or end product 66as described above with respect to FIG. 4. Nucleic acid extracted fromthe sample may be placed within a reaction vessel (e.g., a PCR tube)along with a light-emitting species that emits light in a stoichiometricrelationship with the target nucleic acid, which may be a DNA sequenceassociated with a target organism (e.g., a bacterial genus or species).In some examples, the sample may be an enriched sample derived from asample of food or feed raw material, end product, water or productionenvironment. For example, the sample placed in the reaction vessel maybe an enriched sample from a culture derived from the initial sample. Insome such examples, the estimated quantity of the organism may be anestimated initial quantity of the organism. In some examples, thereaction vessel containing a sample and a light-emitting speciescollectively may be referred to herein as a “biological assay.” Detector16 of nucleic acid amplification device captures a data set comprisingtime-series measurement samples of the light emitted by thelight-emitting species over one or more amplification cycles andtransmits the data set to computing device 42 of user device 20, acomputing device of access point 24, or any other suitable computingdevice (72).

In the example of user device 20, one or more of processors 23, signalprocessing module 52, and/or other components of computing device 42 mayapply a trained machine learning system to the data set to estimate thequantity of the target organism in the sample (74). In some examples,the data set may include one or more data subsets associated with one ormore different portions or phases of the amplification cycle, such asone or more portions or phases before, during, and/or after a peakamplitude of light emitted over the amplification cycle. Including datasubsets from such different portions or phases of the amplificationcycle may contribute to the accuracy with which the trained machinelearning system may estimate the quantity of the target organism in thesample, as further described below with respect to FIGS. 11 and 12.

FIG. 5B is a flow diagram illustrating an example technique fordetecting inhibited biological assays, in accordance with one aspect ofthe disclosure. The example approach of FIG. 5B may be carried out usinga nucleic acid amplification device such as nucleic acid amplificationdevice 8 of the systems of FIGS. 1 and 2. As described above withrespect to FIG. 1, nucleic acid amplification device 8 may be a nucleicacid amplification device of any suitable type and may be configured tocarry out any suitable nucleic acid amplification technique, such asLAMP or PCR. Although described in the context of the systems of FIG. 1,the example technique of FIG. 5B may be carried out using any suitablenucleic acid amplification device and computing device. More specificaspects and examples of the technique generally illustrated in FIG. 5Bare described below with respect to 10A-10C.

In the example approach of FIG. 5B, nucleic acid amplification device 8amplifies a target nucleic acid within an enriched sample withinreaction chamber 10 and detector 16 obtains a data set includingmeasurements representative of (80). In some examples, the sample may bederived from food production environment 60, raw material 62, or endproduct 66 as described above with respect to FIG. 4. Nucleic acidextracted from the sample may be placed within a reaction vessel (e.g.,a PCR tube) along with a light-emitting species that emits light in astoichiometric relationship with the target nucleic acid, which may be aDNA sequence associated with a target organism (e.g., a bacterial genusor species). In some examples, the sample may be an enriched samplederived from a sample of food or feed raw material, end product, wateror production environment. For example, the sample placed in thereaction vessel may be an enriched sample from a culture derived fromthe initial sample.

Detector 16 of nucleic acid amplification device 8 captures a data setcomprising time-series measurement samples of the light emitted by thelight-emitting species over one or more amplification cycles andtransmits the data set to computing device 42 of user device 20, acomputing device of access point 24, or any other suitable computingdevice (82). In some examples, the data set may correspond to aparticular portion or phase of the amplification cycle, such an initialportion or phase occurring at the beginning of the amplification cycleand a subsequent phase. In some example nucleic acid amplificationtechniques (e.g., a LAMP technique), a light-emitting species, such asluciferin, may emit light as nucleic acid detection deice 8 heats to atemperature at which the amplification cycle is carried out.

In the example of user device 20, one or more of processors 23, signalprocessing module 52, and/or other components of computing device 42 mayapply a trained machine learning system to the data set to determinewhether the biological assay tested negative for the target nucleic aciddue to matrix inhibition but would have tested positive for the targetnucleic acid had matrix inhibition not been present (84) or a truenegative. In some examples, the data set may be associated with abackground or baseline signal occurring during a first portion of thebiological assay (e.g., within about the first five minutes of thebiological assay), as further described below with respect to FIGS. 8,9, and others. It should be noted that what is described is inhibitionbased on inhibitory materials in a sample. The techniques described maybe applied to other forms of inhibition, and to other reasons formisclassification of samples in nucleic acid amplification assays.

FIGS. 6 and 7 are conceptual drawings illustrating representativefeatures of example nucleic acid amplification techniques that may beused with the systems and methods described herein. Technical aspects ofan example LAMP technique are described below with respect to FIG. 6,such as to the extent that such technical aspects may be relevant toarriving at the example of FIG. 6. FIG. 7 illustrates aspects of anexample qPCR technique that may be used with the systems and methodsdescribed herein. Technical aspects of an example qPCR technique arediscussed below with respect to FIG. 7, such as to an extent that suchtechnical aspects may be relevant to arriving at the example of FIG. 7.However, it should be understood that the systems and methods describedherein may be used with any suitable nucleic acid amplificationtechnique, and are not limited to the particular examples described withrespect to FIGS. 6 and 7.

LAMP uses strand-displacing Bst DNA polymerase and four to six primersto produce continuous DNA amplification at a constant temperature (i.e.,under isothermal conditions). In LAMP techniques, amplification anddetection of a target nucleic acid can be completed in a single step, byincubating a mixture of a sample, primers, a DNA polymerase with stranddisplacement activity, and substrates at a constant temperature (about65° C.). In some examples, LAMP may provide high amplificationefficiency, with DNA being amplified 10⁹-10¹⁰ times in 15-60 minutes.Because of its high specificity, the presence of amplified product canindicate the presence of target gene.

In LAMP, four different primers recognize six distinct regions in atemplate (i.e., target) DNA sequence and two loop primers recognize twoadditional sites in corresponding single stranded loop regions duringLAMP. The four different primers that recognize the six distinct regionsof the target DNA may include a Forward Internal Primer (FIP), a ForwardOuter Primer (F3; aka FOP), a Backward Inner Primer (BIP), and aBackward Outer Primer (B3; aka BOP). The two loop primers includeForward Loop Primer (FLP) and Backward Loop Primer (BLP). In contrast,PCR and qPCR each use non-strand displacing Taq DNA polymerase and twocorresponding primers, a forward primer and a backward primer torecognize two distinct regions. In addition, qPCR uses a probe (e.g., afluorescence-emitting molecular beacon probe, a fluorescence-emittinghydrolysis probe, a primer carrying a fluorescence-emitting probeelement, or another suitable probe that includes a fluorescent moiety)having specificity to a third distinct region.

The two loop primers FL and BL may bind to additional sites during LAMPand accelerate reactions. For example, primers containing sequencescomplementary to the single stranded loop region (either between the B1and B2 regions, or between the F1 and F2 regions) on the 5′ end of adumbbell-like structure formed during LAMP may provide an increasednumber of starting points for DNA synthesis during a LAMP technique. Forexample, an amplified product containing six loops (not shown) may beformed during LAMP. In example techniques in which loop primers FL andBL are not used, four out of six of such loops would not be used.Through the use of loop primers, all the single stranded loops can beused as starting points for DNA synthesis, thereby reducingamplification time. For example, the time required for amplificationwith loop primers may be about one-third to about one-half of the timerequired for amplification in examples in which loop primers are notused. In some examples, with the use of loop primers, amplification maybe achieved within 30 minutes.

FIG. 6 illustrates real-time detection of nucleic acid amplificationduring a LAMP amplification cycle based on measurements ofbioluminescence intensity over time, in accordance with one aspect ofthis disclosure. In an example LAMP technique, isothermal DNAamplification releases pyrophosphate (PPi) as a byproduct. The byproductPPi is then converted to adenosine triphosphate (ATP) by the enzymeATP-sulfurylase in the presence of adenosine 5′-phosphosulfate. In onesuch example approach, a biological assay having a sample being analyzedfor a target nucleic acid may be adapted to include the luciferaseenzyme and its substrate luciferin, the latter of which may be used asthe light-emitting species in the example systems and methods describedherein. Since ATP is a co-factor for the reaction of the luciferaseenzyme and bioluminescence-producing luciferin, the conversion of PPi toATP during an amplification cycle of a LAMP technique drives theemission of bioluminescence. This emission of bioluminescence may bedetected by a detector of a nucleic acid amplification device configuredfor LAMP, such as detector 16 of nucleic acid amplification device 8 ofFIGS. 1 and 2, and data representing time-series measurements of thebioluminescence are stored as a data set. In some examples, themechanism for generating light during a LAMP technique illustrated inFIG. 6 may provide one or more other benefits, such as enablingreal-time detection of nucleic acid amplification occurring during theLAMP amplification cycle over a relatively short period of time, such asabout 15 minutes.

Time-series measurements of relative light units (RLU) emitted by thelight-emitting species (e.g., luciferin) in a biological assaycontaining the target nucleic acid are depicted in curve 90. Time-seriesmeasurements of relative light units (RLU) emitted by the light-emittingspecies (e.g., luciferin) in a control not containing the target nucleicacid are depicted in baseline curve 92. As shown by curve 90,exponential amplification of the target nucleic acid during the LAMPamplification cycle produces a bioluminescence signal having both arapid increase in RLU and a rapid decrease in RLU. Portions of curve 90prior to and/or after the peak may be associated with a background orbaseline portion of the light signal and may be used in systems andmethods described herein for training and using a machine-learningsystem to determine whether a sample is inhibited by a matrix. In someexamples, the time-to-peak RLU emission corresponds to the quantity ofthe target organism. For example, a relatively greater quantity of thetarget organism may produce a shorter time-to-peak RLU emission. Thus,one or more aspects of curve 90, such as the time-to-peak or amplitude,may be used in training a machine learning system to estimate a quantityof a target organism in examples in which quantitating the targetorganism may be desirable.

In some examples, the data set used to train a machine learning systemsuch as a system includes data captured as a set of time-seriesmeasurement samples of bioluminescence captured across the entirety ofthe amplification cycle. In one such example, luminescence measurementsare taken approximately every 5 seconds, which may be accumulated asmeasurements at 10, 15, 20, and/or 25 second intervals across theamplification cycle for reporting purposes.

In some example approaches, the data set used to train a machinelearning system such as a system includes time-series measurementsamples of bioluminescence taken across the entirety of the nucleic acidamplification cycle. In other example approaches, the training data setincludes measurements taken during one or more of a first phase 94 ofthe amplification cycle, a second phase 96 of the amplification cycleand a third phase 98 of the amplification cycle. In some such examples,a machine learning system may be trained to estimate a quantity of thetarget organism present in a sample based on samples in each of thefirst, second, and third data subsets, based on the data set of samplestaken across the entire amplification cycle, or based just on samples inthe second subset. In one such example approach, the samples from thesecond subset include a sample taken at T_(max), where T_(max) is thetime during the nucleic acid amplification cycle that the maximumamplitude of the target nucleic acid is detected. Again, samples may betaken approximately every 5 seconds, which may be accumulated tomeasurements from about 10, 15, 20, and/or 25 seconds across theamplification cycle for reporting purposes. Training the machinelearning system based in part on data subsets not associated with peakamplification may provide more robust training than training based onlyon one or more data subsets associated with peak amplification, which inturn may enhance the ability of the trained machine learning system toaccurately estimate an unknown quantity of the target organism inexamples in which quantitating the target organism may be desirable.

A detector, such as detector 16 of nucleic acid amplification device 8,may capture a data set that includes time-series measurement samples ofthe light emitted by the light-emitting species during the amplificationcycle as depicted in curve 90 and transmit the data set to a computingdevice (e.g., computing device 42). In some examples, the data set mayinclude time-series measurement samples associated with portions ofcurve 90 prior to and/or after the peak may be associated with abackground or baseline portion of the light signal and may be used insystems and methods described herein for training and using amachine-learning system to determine whether a sample is inhibited by amatrix. In this manner, the mechanism for generating light during a LAMPtechnique described with respect to FIG. 6 may enable a user to obtainan indication of whether a biological assay is inhibited with greateraccuracy than may be achieved by diluting and re-analyzing the sample,as dilution may cause loss of signal associated with the target nucleicacid if the initial quantity of the nucleic acid preset in the samplewas relatively low. This mechanism also may enable a user to obtain anestimated quantity of the target organism in the sample much sooner thanmay be practicable using traditional pathogen quantitation methods.

In PCR, DNA extension is limited to a specific period of eachthermocycle (i.e., amplification cycle). In PCR, the presence ofinhibitors can prevent the polymerase from extending the DNA in the timeallowed, which may result in incomplete amplification products and mayadd to inhibition caused by an inhibitory matrix, thereby preventing thedetection of the target organism. PCR's temperature cycling and theassociation and disassociation of the polymerase from the DNA templateduring the denaturation step provides many opportunities for inhibitorsto interfere. Inhibition may be less likely to occur in LAMP techniquesthan in PCR- and Immunoassay-based systems. Also, PCR may be more likelyto be subject to interference by the natural fluorescence of some foodsamples and enrichment media. Thus, use of LAMP techniques may provideone or more benefits over the use of PCR techniques in the systems andmethods described herein. However, as discussed above, the use of PCRtechniques in conjunction with the systems and methods described hereinmay provide one or more benefits over traditional pathogen detectionand/or quantitation methods in other examples.

FIG. 7 illustrates detection of nucleic acid amplification during anexample qPCR technique across multiple PCR cycles based on measurementsof fluorescence intensity over time, in accordance with one aspect ofthis disclosure. In some such examples, a light-emitting species may bea fluorescence-emitting hydrolysis probe, such as a TaqMan hydrolysisprobe (available from Thermo Fisher Scientific). During PCR, 5′-3′exonuclease activity of the Taq polymerase cleaves the probe into twoportions, 100A and 100B, during hybridization to a complementary targetDNA sequence. Cleavage of the hydrolysis probe produces a fluorescencesignal, represented in FIG. 7 by curve 102.

As shown by curve 102, amplification of the target nucleic acid duringthe PCR run including multiple amplification cycles produces afluorescence signal. Curve 102 may include several portions or phasesthat reflect corresponding portions or phases of amplification of thetarget nucleic acid. For example, curve 102 may include a first portioncorresponding to an initiation phase of amplification, during which thefluorescence signal may remain below a threshold. Curve 102 further mayinclude a second portion corresponding to an exponential phase ofamplification, during which the fluorescence exceeds the threshold andincreases exponentially. Finally, curve 102 may include a third portioncorresponding to a plateau phase of amplification, during which thefluorescence remains above threshold and slowly increases overadditional amplification cycles. Similar to the example LAMP techniqueof FIG. 6, portions of curve 102 prior to and/or after the exponentialphase may be associated with a background or baseline portion of thelight signal and may be used in systems and methods described herein fortraining and using a machine-learning system to detect matrix inhibitionof a biological assay.

In addition, and as with the example LAMP technique of FIG. 6, a machinelearning system may be trained to estimate a quantity of the targetorganism present in a sample based on each of the first, second, andthird data subsets corresponding to respective ones of the first,second, and third phases of the fluorescence signal as noted above. Themachine learning system may also be trained to estimate a quantity ofthe target organism present in the sample based on a data set offluorescence signal measurements collected across the entirety of theamplification cycle. Training the machine learning system based in parton data subsets not associated with the exponential amplification phaseof a PCR run (e.g., background fluorescence generated at the start ofthe amplification cycle) may provide more robust training than trainingbased only on one or more data subsets associated with peakamplification (e.g., at least a subset containing the exponentialphase), which in turn may enhance the ability of the trained machinelearning system to accurately estimate an unknown quantity of the targetorganism.

FIG. 8A is a flow diagram illustrating an example approach for traininga machine learning system, in accordance with aspects of thisdisclosure. In the example approach of FIG. 8A, a nucleic acidamplification device 8 such as shown in FIG. 1 or FIG. 2 is used to testassays having cell concentrations of a target organism to obtain a dataset for each assay (412). The assays may be from cultures, from matrices(including inhibitory matrices), or from both. Each data set is thenlabeled with a quantity reflective of the quantity of target organismsdetected in each respective array by the nucleic acid amplificationdevice (414). System 6 then trains a machine learning system using thelabeled data sets (416) to estimate a quantity of the target organism inan assay. In some example approaches, the method further includesestimating a quantity of the target organism in an assay using thetrained machine learning system (418). In some example approaches, thequantity of target organism associated with each assay is based on apriori knowledge of the sample (e.g., from a sample having a known cellconcentration). Each data set is therefore labeled based on the a prioriknowledge. In other example approaches, the quantity associated witheach assay is based on quantitative testing of the sample, such asthrough the use of an alternative quantitation method such as, forexample, MPN, and each data set is labeled based on the results providedby the alternative quantitative method.

In some example approaches, quantification of DNA-based assays isperformed using high quality DNA and a single response value from a DNAamplification reporter. This response value, usually fluorescence orbioluminescence, may be based on the signal surpassing a presetthreshold value or on a peak amplitude value. In some examples, it maybe desirable to estimate an initial quantity of more than one strain orspecies (e.g., within a genus) of a target organism in a sample, as morethan one of such strains or species may be pathogenic.

In one example approach, culture preparation included inoculating 10 mLof Buffered Peptone Water (BPW, 3M Company, St. Paul) with a singlecolony from an agar plate corresponding to each strain (Table 1). Theinoculated broths were incubated at 37° C. for 18 h.

TABLE 1 Strain Reference¹ Salmonella enterica subsp. enterica ATCC ®14028 ™ serovar Typhimurium Salmonella enterica subsp. enterica ATCC ®13076 ™ serovar Enteritidis Salmonella enterica subsp. enterica TC 164serovar Hadar Salmonella enterica subsp. enterica ATCC ® 51741 ™ serovarInfantis Salmonella enterica subsp. enterica TC 251 serovar Kentucky¹American Type Culture Collection and Tecra ™ Collection.For enumeration, the cultures were serially diluted in ButterfieldsBuffer and plated onto 3M™ brand Petrifilm™ Aerobic Count (AC) Plates(3M Company) (hereinafter “Petrifilm AC plates”) followingmanufacturer's instructions. The cultures were kept at 4-8° C. untilplate count results were obtained. The counts obtained were used toestimate the number of cells used for the detection using 3M™ brandMolecular Detection Assay 2—Salmonella (3M Company) (hereinafter“MDA2—Sal”). A final plate count was conducted using Petrifilm AC platesat the time of conducting the detection assay. These final plate countswere used for reporting the concentration of cells.

In one example approach, each strain was serially diluted inButterfield's Buffer to approximately 10², 10³, 10⁴, 10⁵ and 10⁶ colonyforming units (CFU) per milliliter. Aliquots from each dilution wereanalyzed using MDA2—Sal following manufacturer's instructions. MDSsoftware supplied by 3M Company was then used to determine thetime-to-peak, a response to the amplification of the target sequence. Adataset of time-to-peak for known concentrations of cells was then usedto train a Decision Forest Regression model and a Boosted Decision Treemodel. Both approaches yielded coefficients of determination ofapproximately 0.75. The same dataset used to train a linear regressionmodel yielded a coefficient of determination R² of approximately 0.2912.Other regression techniques, such as support vector regression, randomforest regression, ridge regression, logistic regression, Lasso, andnearest neighbor regression, may also be used to train models based ondata sets of time-to-peak for known concentrations of cells.

Time-to-peak response is not always the best measure of cell count.Differing matrices (i.e., substances other than a pure culture in asample or molecular components in food sample) may prevent goodagreement between time-to-peak response and actual cell counts. A countof cells of a Salmonella strain may, for instance, produce differenttime-to-peak measurements depending on the matrix in which the cells arelocated. For example, different time-to-peak measurements may resultfrom a particular count of cells of the Salmonella strain in a salmonmatrix versus a shellfish matrix, or in other such matrices. In someexample approaches, measurements of parameters such as light intensityover time across a nucleic acid amplification cycle provide a betterrepresentation of initial cell count. Even then, it may be advantageousto train a machine learning system with different matrices to moreaccurately estimate quantity of a target organism within a particularmatrix.

In some example approaches, each data set includes time-seriesmeasurement samples of the light intensity detected by detector 16during an amplification cycle. Each data set is labeled with known cellconcentration of its respective assay and the labeled data set is thenused to train a machine learning system 25 or 35 as detailed below.Machine learning system 25 or 35 is then used to estimate a quantity ofthe target organism in each assay. In some example approaches, adifferent data set is used for each matrix or type of matrix. A matrixrepresenting target organisms in cheese may be used, for example, totrain a machine learning system 25 or 35 for use in quantitating targetorganisms in a cheese factory.

In some example approaches, each data set includes light intensitymeasurements made over time during one or more amplification cycles. Insome such example approaches, each data set includes the time-seriesmeasurements of light intensity captured across the whole of theamplification cycle. In some example approaches, such data sets alsoinclude measurements made during a period at the start of theamplification cycle where the data is typically either not captured,discarded or otherwise suppressed by nucleic acid amplification device8. In some example approaches, each data set includes light intensitymeasurements made in a first period before T_(max), light intensitymeasurements made in a second period of time including T_(max), andlight intensity measurements made in a third period of time occurringafter T_(max).

FIG. 8B is a flow diagram illustrating an example approach that uses thetrained machine learning system of FIG. 8A to estimate a quantity of atarget organism, in accordance with aspects of this disclosure. In theexample approach of FIG. 8B, the method includes receiving a sample of amatrix (422), such as by a laboratory worker or automated equipment. Thematrix may be, for example, a matrix in which the target organism may befound, such as the poultry rinse matrix, or a portion of a raw materialof a food product or end product of a food product. Upon receiving thematrix, the laboratory worker or equipment adds an appropriateenrichment medium configured to enable growth of the target organismwithin the sample containing the target organism and the matrix to adetectable limit (424). In some examples, such as examples in which aPCR technique is used for amplification of target nucleic acid, anappropriate enrichment medium may have a characteristic of being lesslikely to interfere with the fluorescence emitted during PCR than one ormore otherwise appropriate enrichment media, such as by emitting lessbackground fluorescence relative to other appropriate media. Next, insome example approaches, the worker or equipment prepares a 1:10dilution of the resulting enrichment solution (426). The use of a 1:10dilution may increase the specificity of the trained machine learningsystem for the target organism. Other suitable dilutions may be used,such as 1:100 or 1:1000. The amount of dilution will, in some exampleapproaches, depend on system characteristics such as the type oforganism targeted and the particular amplification technique.

Next, the sample within the enrichment solution is incubated to allowenrichment of the target organism (428). In some examples, the samplemay be incubated at about 35-42° C. for about 4-24 hours, or at anyother suitable temperature and period of time that may enable suitablegrowth of the target organism. In other examples, an enrichment step maynot be used, but instead the nucleic acid may be extracted from a samplewithout enrichment. Following incubation, if used, the sample isanalyzed via, in some example approaches, amplification and detection ofthe target nucleic acid associated with the target organism (430). Forexample, the target nucleic acid may be amplified and detected using anucleic acid amplification device 8 having a light detector 16 such as aMolecular Detection System (MDS) available from 3M Company of St. Paul,Minn. The MDS, for example, may be configured to amplify the targetnucleic acid by carrying out a LAMP technique and may then detectbioluminescence emitted by a light-emitting species within the sample(e.g., luciferin) using detector 16. By combining LAMP withbioluminescence detection, nucleic acid amplification devices such asthe MDS may make molecular detection of foodborne pathogens simpler andfaster, thereby providing users with speed and ease in simultaneouslyidentifying one or more target organisms (e.g., one or more species orstrains of Salmonella, Listeria, Listeria monocytogenes, E. coli O157(including H7), Campylobacter, Cronobacter and/or other targetorganisms) in food and/or environmental samples. In other exampleapproaches, the techniques of FIGS. 8A and 8B are carried out using adifferent LAMP platform or using a PCR platform or a different nucleicacid amplification platform.

In some example approaches, the amplitude of light generated early in anamplification cycle (e.g., before phase 94 or phase 104) may besuppressed (e.g., not recorded) so as to not confuse users withbackground activity. It has been found, however, that such informationmay be helpful in training the machine learning system. Therefore, inone example approach, the data set includes time-series measurementsmade before phase 94 in FIG. 6. In a similar example approach, the dataset includes time-series measurements made before phase 104 in FIG. 7.

In some example approaches, labeled data sets are produced by expertinspection of individual samples on which nucleic acid amplification hasbeen performed. In one such example approach, an expert receives datasets associated with the samples, determines a quantity of organismsand/or target nucleic acid in the sample (via, for example, one of thetraditional quantification techniques described above such as MPN) andlabels each data set with the determined quantity value. The labeleddata sets are then used to train a machine learning system, as depictedin FIG. 8A.

In some example approaches, data sets include time-series measurementstaken at predetermined intervals (e.g., 25 seconds) across the whole ofthe amplification cycle. In other example approaches, data sets includedata selected from certain phases of the amplification cycle. Forinstance, a data set may include data from one or more of phases 94, 96and 98 in FIG. 6 or from one or more of phases 104, 106 and 108 in FIG.7. For example, where (430) includes a LAMP technique, the data set mayinclude one or more data subsets as described with respect to FIG. 6.For example, the data set may include a first data subset representingtime-series measurement samples of light emitted up to a first point intime in the amplification cycle, the first point in time occurring priorto a peak amplitude of the light emitted over the amplification cycle, asecond data subset representing time-series measurement samples of lightemitted after the first point in time but before a second point in timein the amplification cycle, the second point in time occurring after thepeak amplitude, and a third data subset representing time-seriesmeasurement samples of light emitted after the second point in time inthe amplification cycle. A computing device (e.g., processing circuitry30 of external device 28 of FIG. 2 or any other suitable computingdevice) then trains a machine learning system to predict the initialconcentration (i.e., quantity) of the target organism of interest (seeFIG. 8A).

In one example, the computing device may label a data set, and/or one ormore subsets of the data set, with an estimate of the quantity of thetarget organism within the biological assay associated with therespective data set or data subset. The computing device then trains themachine learning system with the labeled data sets (or data subsets)and/or matrix identity to estimate a quantity of the target organismwithin the sample, resulting in a trained model. The computing devicethen may store the parameters of the trained machine learning system toone or more storage components of a system, such as a memory of acomputing device, user device 20, a memory of a computing device ofaccess point 24, and/or to any other suitable location.

In a workflow technique associated with using a trained machine learningsystem to calculate a quantity of the organism of interest, thetechnique of FIG. 8B includes carrying out steps 422-436 insubstantially the same way as when data sets were collected to train themachine learning system. The matrix at (422) may be a sample of a rawfood material, an end food product, or an environmental sample that maycontain a target organism of interest instead of a known quantity of thetarget organism. In such examples, a nucleic acid amplification anddetection system, such as the MDS or another system configured to carryout LAMP or PCR and detect light emitted by light-emitting speciesduring one or more amplification cycles, may capture a data set, thedata set comprising time-series measurement samples of the light emittedby the light-emitting species during the amplification cycle and analyzethe data set (430). The data set is then analyzed based on the trainedmachine learning model to arrive at an estimate of the quantity of thetarget organism in the matrix (436).

In some such examples the data set may include one or more data subsetscorresponding to one or more portions of an amplification cycle, such asin a manner similar to data subsets with which the machine learningsystem is trained. For example, a data set corresponding to a samplecontaining an unknown quantity of a target organism may include a firstdata subset representing time-series measurement samples of lightemitted up to a first point in time in the amplification cycle, thefirst point in time occurring prior to a peak amplitude of the lightemitted over the amplification cycle, a second data subset representingtime-series measurement samples of light emitted after the first pointin time but before a second point in time in the amplification cycle,the second point in time occurring after the peak amplitude, and a thirddata subset representing time-series measurement samples of lightemitted after the second point in time in the amplification cycle. Acomputing device configured to receive the first, second, and third datasubsets (e.g., computing device 42 of user device 20, a computing deviceof access point 24, or any other suitable computing device) applies thetrained machine learning system to the data subsets (436) and calculatesthe concentration (e.g., quantity) of the target organism of interest inthe sample. In some examples, the computing device then may store one ormore such estimated quantities to one or more storage components of asystem, such as a memory of an MDS, a memory of a computing device userdevice 20, a memory of a computing device of access point 24, and/or toany other suitable location.

In some example approaches, separate machine learning systems aretrained as a function of the type of matrix being tested. For instance,a separate system may be trained for testing cheese, or for testingfeed, with the parameters of each machine language machine learningsystem stored in memory based on the type of matrix being tested.

As noted above, it can be critical to reduce or eliminate falsenegatives while testing for the presence of target organisms in asample. FIG. 9 illustrates test results from an example pathogendetection system indicative of a biological assay error 110, aninhibited and invalid biological assay 112, an inhibited but validbiological assay 114, and a valid and uninhibited biological assay 116,in accordance with one aspect of this disclosure. Some nucleic acidamplification and detection systems are configured to analyze abackground or baseline signal (in RLU) the first five minutes of thebiological assay. During this time, a peak in RLU is detected as part ofthe biochemistry engineered in the reaction and the biological assay isconsidered acceptable for analysis. If the peak is absent, an errormessage may be reported to the user. However, a matrix can be inhibitoryto the biological assay and still produce a valid signal and a falsenegative result might be reported to the user, as in the example ofinhibited but valid biological assay 114. Additional challenges may bepresent with matrices that generate a high baseline signal, such asmatrices containing high concentrations of inorganic phosphorus, andthose that will generate a low baseline, such as dark or turbidmatrices. In both such cases, the initial peak height or an average orotherwise statistically combined value alone are not reliable indicatorsof a valid or inhibited biological assay (i.e. a high or low backgroundcan yield a true negative (or positive) or a false negative result).

The reporting signal (here, the light emitted by the light-emittingspecies at the initial portion of the amplification cycle) may appear asa spike or peak in the output curve, as illustrated in FIG. 9 andfurther as discussed below with respect thereto. Such a spike or peakmay be considered part of the background or baseline of the signalrepresentative of the light within the sample during the amplificationcycle, as the light corresponding to such an initial spike or peak isnot necessarily associated with amplification of the target nucleicacid. The initial spike or peak in the output curve may be indicative ofthe validity of the biological assay but not necessarily indicative ofwhether the biological assay is inhibited. For example, the spike orpeak in the output curve at the initial portion of the amplificationcycle may not occur, which may indicate that the biological assay isinvalid or inhibited. However, an initial spike or peak in the outputcurve may occur even in inhibited biological assays, although in suchexamples, the spike may not be large enough (e.g., have insufficientwidth and/or amplitude) to be indicative of a non-inhibited biologicalassay.

Thus, the presence or absence of an initial spike may not be asufficient criterion for detecting inhibition of a biological assay.Moreover, a subsequent background or baseline value of the output curvemay not be a sufficient criterion on which inhibition of a biologicalassay may be determined. Thus, in some examples, a data set used totrain a machine-learning system or used by a trained machine-learningsystem may correspond to an initial portion or phase occurring at thebeginning of the amplification cycle and a subsequent phase occurringafter the initial spike or peak.

FIGS. 10A and 10B are flow diagrams illustrating example techniques forusing data sets that tested negative for a target organism to train amachine-learning system to detect inhibited biological assays. In otherexample approaches, the data sets capture time-to-peak measurements ofeach assay. In some example approaches, provide time-series measurementsof reporter signal intensity across some or substantially all of theamplification cycle. In yet other example approaches, the data setsprovide time-series measurements of reporter signal intensity acrossportions of two or more amplification cycles. In yet other exampleapproaches, the data sets provide time-series measurements of reportersignal intensity across substantially all of two or more amplificationcycles. In some example approaches, the data sets include dataassociated with background or baseline signals captured by the nucleicacid amplification device.

In some example approaches of FIGS. 10A and 10B, labeled data sets areproduced by expert inspection of individual samples on which nucleicacid amplification has been performed. In one such example approach, anexpert receives data sets associated with samples that tested negativefor the target organism, determines whether the sample is a falsenegative and labels each data set as true negative or false negativeaccordingly. The labeled data sets are then used to train a machinelearning system, as depicted in FIGS. 10A and 10B.

In the example flowchart of FIG. 10A, data sets from assays that testednegative for the target organism were used to train a machine-learningsystem to detect inhibited biological assays. In one example approach,the machine-learning system was trained to estimate the probability thata background signal of a biological assay corresponds to amatrix-inhibited biological assay (i.e., rendering the biological assayunable to produce a positive reaction). In one example approach, theplurality of data sets included 2,186 separate data sets, each data setincluding analyses of raw data (RLU over time). In one example approach,the plurality of data sets were taken from 22 different manufacturinglots, and included 701 inhibited (false negative) samples and 1,485 truenegative samples.

Generally, the data sets used to train a machine-learning system todetect false negatives come from biological assays that have testednegative for the target nucleic acid. As noted above, such negativeresults may include both truly negative results and false negativeresults. The false negative results may, in some instances, beassociated with biological assays that tested negative for the targetnucleic acid due to matrix inhibition and that would have testedpositive had matrix inhibition not been present.

A representative data set will be discussed next. In some exampleapproaches, device 8 captures data indicative of a reporting signal asraw data (RLU over time) time-series measurements of the reportingsignal. In one LAMP-related approach, the raw data representsmeasurements, by a detector, of light emitted within the respectivebiological assay during an amplification cycle performed by a nucleicacid amplification device on the respective biological assay.

In one example approach, the raw data is received as a data set (130).In some example approaches, data sets from different sources are trimmedto a common run time (such as 59 minutes) (132). The data sets are log10transformed (134). In one example approach, the data sets of theplurality of data sets are randomly partitioned with a stratified splitto ensure that a representative number of inhibited (i.e., falsenegative) samples are present in each partition, as they may beunderrepresented in a plurality of data sets (136). A training platform,such as a Microsoft Azure™ platform is then used to train one or moremachine-learning systems using the optimal parameters found by a tuningmodel (138). However, it should be understood that any suitable trainingplatform may be used in carrying out methods for training amachine-learning system as described herein in other examples.

In some examples, training a machine-learning system may includelabeling, as false negative data sets, those data sets from theplurality of data sets that are associated with biological assays thattested negative for the target nucleic acid due to matrix inhibition andthat would have tested positive had matrix inhibition not been present,and labeling, as true negative data sets, those data sets from theplurality of data sets that are associated with biological assays thattested negative for the target nucleic acid and that were not inhibited,such that training the machine-learning system with the true and falsenegative data sets enables the machine-learning system distinguishbetween biological assays of the target nucleic acid that truly testnegative for the target nucleic acid and biological assays of the targetnucleic acid that falsely test negative for the target nucleic acid dueto matrix inhibition (140).

In the example of FIG. 10A, the trained models were cross validated bypartitioning the data set in 10 slices that are individually used asvalidation set while the other nine are used to train the model (142).The average of the performance of the 10 partitions is presented forvarious models in Table 2 below. Finally, the plurality of data sets maybe evaluated with the best-performing model to test the trainingmethods, one or more of which may be stored to algorithm storage(described below with respect to FIG. 11) and/or to a user device (144).It should be understood that aspects of the plurality of data sets usedin iteration of the example technique of FIG. 10A described above areillustrative in nature and should not be considered limiting to thetechnique of FIG. 10A. Any suitable plurality of data sets includinginhibited (false negative) and true negative data sets from any suitablesources may be used in the technique of FIG. 10A. Additionally, oralternatively, one or more steps of the example technique of FIG. 10Amay be optional (e.g., cutting run time (132) or others). In someexamples, an example technique for training a machine-learning systemmay include one or more additional steps not illustrated in FIG. 10A.

TABLE 2 Target analyte of assays in the data set. True True False FalseAccuracy Precision Recall Model Positive Negative Positive Negative (%)(%) (%) Two-class 1468 669 32 17 97.8 97.9 98.9 boosted decision treeTwo-class 1472 662 39 13 97.6 97.4 99.1 decision forest Two-class 1455644 57 30 96 96.2 98 regression Two-class 1457 664 37 28 96.2 97 97.4locally deep support vector machine

In the example approach of FIG. 10B, a nucleic acid amplification device8 in system 6 was used to test biological assays of samples. Data setsassociated with samples that tested negative for a target organism areexamined by an expert to determine if the test result was a truenegative or a false negative, and each data set is labeled accordingly(150). Each data set may include measurements, performed on theassociated biological assay by a nucleic acid amplification device of aspecified type and collected over at least a portion of a nucleic acidamplification cycle, of the target nucleic acid detected within theassociated biological assay and associated with a target organism.System 6 then trains a machine learning system using the labeled datasets (152). In some example approaches, the method further includesdetecting inhibited biological assays and issuing a result indicatingwhether the biological assay tested negative for the target nucleic aciddue to matrix inhibition using the trained machine learning system(154).

FIG. 10C is a flow diagram illustrating an example technique for usingthe trained machine learning system to detect inhibited biologicalassays, in accordance with one aspect of this disclosure. In one suchexample approach of using a machine learning system to detect inhibitedbiological assays and to issue a result indicating whether a biologicalassay tested negative for a target nucleic acid due to matrixinhibition, the technique of FIG. 10C includes receiving a sampleincluding a matrix and a quantity of a target nucleic acid (160), suchas by a laboratory worker or automated equipment. The matrix may be, forexample, a matrix in which a target organism associated with the targetnucleic acid may be found, such a portion of a raw material of a foodproduct or end product of a food product. Upon receiving the sample, thelaboratory worker or equipment adds an appropriate enrichment mediumconfigured to enable growth of the target organism within the samplecontaining the target organism and the matrix to a detectable limit(162). In some examples, such as examples in which a PCR technique isused for amplification of target nucleic acid, an appropriate enrichmentmedium may have a characteristic of being less likely to interfere withthe fluorescence emitted during PCR than one or more otherwiseappropriate enrichment media, such as by emitting less backgroundfluorescence relative to other appropriate media, which may help enablean accurate detection of whether the biological assay is inhibited dueto matrix inhibition. Next, in some example approaches, the worker orequipment prepares a 1:10 dilution of the resulting enrichment solution(164).

Next, the sample within the enrichment solution is incubated to allowenrichment of the target organism (166). In some examples, the samplemay be incubated at about 35-42° C. for about 4-24 hours, or at anyother suitable temperature and period of time that may enable suitablegrowth of the target organism. In other examples, an enrichment step maynot be used, but instead the nucleic acid may be extracted from a samplewithout enrichment. Following incubation, if used, the sample isanalyzed via, in some example approaches, amplification and detection ofthe target nucleic acid associated with the target organism (168). Forexample, the target nucleic acid may be amplified and detected using anucleic acid amplification device 8 having a light detector 16 such asthe MDS. The MDS, for example, may be configured to amplify the targetnucleic acid by carrying out a LAMP technique and may then detectbioluminescence emitted by a light-emitting species within the sample(e.g., luciferin) using detector 16. Next, system 6 applies the machinelearning system trained to detect inhibited biological assays and issuea result indicating whether the biological assay tested negative for thetarget nucleic acid due to matrix inhibition (i.e., produced a falsenegative result). By combining LAMP with bioluminescence detection,nucleic acid amplification devices such as the MDS may make moleculardetection of foodborne pathogens via detection of inhibited biologicalassays and/or quantitation of a target organism simpler and faster,thereby providing users with speed and ease in simultaneouslyidentifying one or more target organisms (e.g., one or more species orstrains of Salmonella, Listeria, Listeria monocytogenes, E. coli O157(including H7), Campylobacter, Cronobacter and/or other targetorganisms) in food and/or environmental samples. In other exampleapproaches, the techniques of FIGS. 9 and 10A-10C are carried out usinga different LAMP platform or using a PCR platform or a different nucleicacid amplification and detection platform.

In some example approaches, each data set includes light intensitymeasurements made over time during one or more amplification cycles. Insome such example approaches, each data set includes the time-seriesmeasurements of light intensity captured across the whole of theamplification cycle. In some example approaches, such data sets alsoinclude measurements made during a period at the start of theamplification cycle where the data is typically either not captured,discarded or otherwise suppressed by nucleic acid amplification device8. In some example approaches, each data set includes light intensitymeasurements made in a first period before T_(max), light intensitymeasurements made in a second period of time including T_(max), andlight intensity measurements made in a third period of time occurringafter T_(max).

In some example approaches of FIG. 10C, data sets include time-seriesmeasurements taken at predetermined intervals (e.g., 25 seconds) acrossthe whole of the amplification cycle. In other example approaches, datasets include data selected from certain phases of the amplificationcycle. For instance, a data set may include data from one or more ofphases 94, 96 and 98 in FIG. 6 or from one or more of phases 104, 106and 108 in FIG. 7. For example, where (152) includes a LAMP technique,the data set may include one or more data subsets as described withrespect to FIG. 6. For example, the data set may include a first datasubset representing time-series measurement samples of light emitted upto a first point in time in the amplification cycle, the first point intime occurring prior to a peak amplitude of the light emitted over theamplification cycle, a second data subset representing time-seriesmeasurement samples of light emitted after the first point in time butbefore a second point in time in the amplification cycle, the secondpoint in time occurring after the peak amplitude, and a third datasubset representing time-series measurement samples of light emittedafter the second point in time in the amplification cycle. A computingdevice (e.g., processing circuitry 30 of external device 28 of FIG. 2 orany other suitable computing device) then trains a machine learningsystem to predict the initial concentration (i.e., quantity) of thetarget organism of interest (154). For example, the computing device maylabel a data set, and/or one or more subsets of the data set, with anestimate of the quantity of the target organism within the biologicalassay associated with the respective data set or data subset. Thecomputing device then trains the machine learning system with thelabeled data sets (or data subsets) and/or matrix identity to estimate aquantity of the target organism within the sample, resulting in atrained model. The computing device then may store the parameters of thetrained machine learning system to one or more storage components of asystem, such as a memory of a computing device, user device 20, a memoryof a computing device of access point 24, and/or to any other suitablelocation.

In a workflow technique associated with using a trained machine learningsystem to determine whether a negative results is a false negative, thetechnique of FIG. 10C includes carrying out steps 160-172 substantiallyas described above with respect to an example technique for training themachine learning system, although the matrix at (160) may be a sample ofa raw food material, an end food product, or an environmental samplethat may contain a target organism of interest instead of a knownquantity of the target organism. In such examples, a nucleic acidamplification and detection system, such as the MDS or another systemconfigured to carry out LAMP or PCR and to detect light emitted bylight-emitting species during one or more amplification cycles, maycapture a data set, the data set comprising time-series measurementsamples of the light emitted by the light-emitting species during theamplification cycle and analyze the data set (168). The data set is thenanalyzed based on the trained machine learning model discussed in thecontext of FIGS. 8A and 8B above to arrive at either an estimate of thequantity of the target organism in the matrix, or an indication ofwhether a threshold amount of the target organism is present. If thesample tests negative for the target organism, a check is made at (172)to determine if the results is a false negative (by, for instance,applying the machine learning system trained at (138) of FIG. 10A). Inone such example approach, false negatives are flagged and reported to auser via user device 20.

In some such examples, the data set may include one or more data subsetscorresponding to one or more portions of an amplification cycle, such asin a manner similar to data subsets with which the machine learningsystem is trained. For example, a data set corresponding to a samplecontaining an unknown quantity of a target organism may include a firstdata subset representing time-series measurement samples of lightemitted up to a first point in time in the amplification cycle, thefirst point in time occurring prior to a peak amplitude of the lightemitted over the amplification cycle, a second data subset representingtime-series measurement samples of light emitted after the first pointin time but before a second point in time in the amplification cycle,the second point in time occurring after the peak amplitude, and a thirddata subset representing time-series measurement samples of lightemitted after the second point in time in the amplification cycle. Acomputing device configured to receive the first, second, and third datasubsets (e.g., computing device 42 of user device 20, a computing deviceof access point 24, or any other suitable computing device) applies thetrained machine learning system to the data subsets (136) and calculatesthe concentration (e.g., quantity) of the target organism of interest inthe sample. In some examples, the computing device then may store one ormore such estimated quantities to one or more storage components of asystem, such as a memory of an MDS, a memory of a computing device userdevice 20, a memory of a computing device of access point 24, and/or toany other suitable location.

In some example approaches, the amplitude of light generated early in anamplification cycle (e.g., before phase 94 or phase 104) may besuppressed (e.g., not recorded) so as to not confuse users withbackground activity. It has been found, however, that such informationmay be helpful in training the machine learning system. Therefore, inone example approach, the data set includes time-series measurementsmade before phase 94 in FIG. 6. In a similar example approach, the dataset includes time-series measurements made before phase 104 in FIG. 7.

In some example approaches of FIG. 10C, separate machine learningsystems are trained as a function of the type of matrix being tested.For instance, a separate system may be trained for testing cheese, orfor testing feed, with the parameters of each machine language machinelearning system stored in memory based on the type of matrix beingtested.

FIG. 11 is a block diagram illustrating a device training system, inaccordance with one aspect of this disclosure. In the example shown inFIG. 11, device training system 200 includes a training module 202connected to labeled data sets module 204 via link 206. In some exampleapproaches, device training system 200 is connected via a link 208 to auser device 210. In one example approach, training module 202 includes acomputing device, one or more storage components and a user interface.For example, device training system 200 may include a computing deviceof external device 28 and memory 32 of FIG. 2. In one example approach,training module 202 receives labeled data sets from labeled data setsmodule 204. In some such example approaches, each labeled data set islabeled as a false negative data set associated with a biological assaythat tested negative for the target nucleic acid due to matrixinhibition or as a true negative data set associated with a biologicalassay that correctly tested negative for the target nucleic acid. Inexamples in which the target organism is quantified, the data setincludes a target organism quantity associated with a sample andmeasurements of light detected during an amplification cycle of thesample by a nucleic acid amplification device 8. Training module 202trains a machine learning system with the labeled data sets and storesparameters associated with the machine learning system in storage 212,which is connected to training module 202 via link 214.

FIGS. 12-25 illustrate example data sets, the application of trainedmachine-learning systems to such example data sets to detect falsenegatives, and the results of such applications of trainedmachine-learning systems to such example data sets. FIGS. 12-15illustrate an example analysis of a test batch of environmental samplestested for one or more target nucleic acids associated with Listeriaspecies, including initial results, application of a matrix control tothe initial results, application of a trained machine-learning system tothe test batch of samples, and application of the trainedmachine-learning system that was applied to the test batch of samples toa training data set. FIGS. 16-25 illustrate data sets of a plurality ofdata sets that was used to train a machine-leaning system, such as thetrained machine-learning system applied to the test batch ofenvironmental samples of FIGS. 12-15.

FIG. 12 illustrates an analysis report for environmental samplesproduced by an example pathogen detection system, in accordance with oneaspect of this disclosure. In the example approach shown in FIG. 12, anexample analysis report is associated with a batch of ten environmentalsamples tested for one or more target nucleic acids associated withListeria species. Such an analysis report may be produced, for instance,by an example pathogen detection system, such as the systems of FIGS. 1and/or 2, in accordance with one aspect of this disclosure. In theexample of FIG. 12, ten samples were analyzed in duplicates using 10 or20 μL of enrichment broth into a suitable lysis tube. Additionalduplicates of each sample were analyzed using an external matrix controlto determine inhibitory properties of the samples towards the assay.Samples were loaded starting from A1 in the grid illustrated in FIG. 12and continuing down the column. The first group in the grid of FIG. 12corresponds to assays for a target nucleic acid associated with Listeriaspecies in which 20 μL of the enriched sample was loaded to the assay.The second group in the grid of FIG. 12 corresponds to Listeria speciesassays from the same enriched samples where 10 μL were loaded.Duplicates of these groups were loaded into the external controls in thethird and fourth groups.

During analysis of the batch of ten environmental samples of FIG. 12,Sample #1 was found to be inhibitory to the assay. Determination ofSample #1 as being inhibited was carried out using an external matrixcontrol (labeled as “MC” in FIG. 12), with inhibition of the sampleindicated by a slash through the “MC” label associated with Sample #1 inFIG. 12.

Detection software, such as the MDS detection software that produced theanalysis report of FIG. 12, may be designed to identify samples as beinginhibited when the inhibited matrix control is linked with the sample ID#. Such an approach, however, requires every new sample to be associatedwith an external matrix control MC and to be labelled accordingly. Sinceenvironmental samples vary, this approach may be difficult to apply fora laboratory.

TABLE 3 Results for Listeria detection for environmental samples of FIG.12 Result 20 □L sample to lysis 10 □L sample to lysis Sample ID MDA 2LIS MC MDA 2 LIS MC 1 Negative Inhibited Negative Inhibited 2 PositiveValid Positive Valid 3 Positive Valid Positive Valid 4 Negative ValidNegative Valid 5 Negative Valid Negative Valid 6 Negative Valid NegativeValid 7 Negative Valid Negative Valid 8 Positive Valid Positive Valid 9Positive Valid Positive Valid 10 Positive Valid Positive Valid Dilutedsample 20 □l of 1:10 to lysis 20 □l of 1:100 to lysis 1 Positive ValidPositive ValidAs in the Salmonella example of above, in this example approach, thecultures were serially diluted in Butterfields Buffer and plated ontoPetrifilm AC plates following manufacturer's instructions. The cultureswere kept at 4-8° C. until plate count results were obtained. The countsobtained were used to estimate the number of cells used for thedetection using 3M™ brand Molecular Detection Assay 2—Listeria (3MCompany) (hereinafter “MDA2—Lis”). A final plate count was conductedusing Petrifilm AC plates at the time of conducting the detection assay.These final plate counts were used for reporting the concentration ofcells.

In this example run, the external matrix control was not linked toSample #1 and the detection software falsely labeled Sample #1 as beingnegative for a target nucleic acid associated with Listeria species dueto matrix inhibition of the sample. This outcome illustrates one issuewith relying only on external matrix controls for detecting inhibitionof samples.

FIG. 13 illustrates a workflow depicting an application of a matrixcontrol and dilution to the samples shown in the analysis report of FIG.12, in accordance with one aspect of this disclosure. As discussedabove, a user may choose to conduct further analysis of a sampledetermined to be inhibited (e.g., Sample #1 of FIG. 12), such as todetermine whether the target nucleic acid is present in the sample. Forexample, the sample may be diluted and re-analyzed. Dilution of thesample may help alleviate the inhibitory effect of the matrix, althoughdilution may also cause loss of signal associated with the targetnucleic acid if the initial quantity of the nucleic acid preset in thesample is relatively low. In the example of FIG. 13, Sample #1 (outlinedin dashed box 220) was diluted (1:10 and 1:100) in duplicates withcorresponding external matrix controls (222) for each replicate. Theresult of the nucleic acid amplification and detection assay run on thedilution of Sample #1 was positive for Listeria species, as shown inFIG. 13 at 224.

FIG. 14 illustrates results of an application of a trained two-classdecision forest algorithm to a collection of data sets, in accordancewith one aspect of this disclosure. To arrive at the results illustratedin FIG. 14, the samples of FIGS. 12 and 13 were further analyzed using atrained machine-learning system. As illustrated in FIG. 14, the trainedmachine-learning system was able to discriminate between the truenegative results 230 from biological assays correctly identified astesting negative for the target nucleic acid from the false negativeresults resulting from inhibited biological assays 232. In this example,the trained machine-learning system used was a trained two-classdecision forest algorithm trained according to the example technique ofFIG. 10A.

FIG. 15 illustrates results of an application of the trained two-classdecision forest algorithm applied in the example of FIG. 14 to theplurality of training data sets described with respect to the exampletechnique of FIG. 10A. The performance of the trained machine-learningsystem on the plurality of training data sets is shown in FIG. 15. Asnoted above in Table 2, the trained two-class decision forest algorithmprovided 97.6% accuracy, 97.4% precision, and 99.1% recall when appliedto the plurality of training data sets, clustering data sets determinedto be true negatives in a cluster 240 as shown near the top of thechart. The algorithm clustered data sets determined to be associatedwith biological assays that tested negative for the target nucleic aciddue to matrix inhibition in clusters 242 and 244 as shown near thebottom of the chart.

FIGS. 16-25 illustrate the application of the machine learning systemsof FIGS. 10A-10C to different collections of data sets. FIG. 16illustrates an example of a collection of data sets, wherein each dataset is represented by a curve representing light intensity over timeduring one or more nucleic acid amplification cycles, each curvecorresponding to a sample, in accordance with one aspect of thisdisclosure. In the examples shown in FIG. 16, each data set follows acurve that reaches a peak before approaching a steady state value oflight intensity.

FIG. 17 illustrates results of an application of the trainedmachine-learning technique of FIGS. 10A-10C (e.g., trained according tothe example technique of FIG. 10A) to the data sets of FIG. 16, inaccordance with one aspect of this disclosure. As shown in FIG. 17, thetrained machine-learning algorithm classified all of the data sets ofFIG. 16 as being a true negative (i.e., corresponding to a biologicalassay in which the target nucleic acid is either not present or notpresent at above a threshold level) at a relatively high probability.

FIG. 18 illustrates an analysis report produced by an example pathogendetection system for a collection of data sets taken from samplesassociated with matrix ingredients known to be inhibitory, in accordancewith one aspect of this disclosure. Such matrix ingredients may includecompounds such as greases, sanitizers, spices, pigments, enzymes, andother ingredients that may occur within a food, feed, or environmentalsample to be tested for a target nucleic acid. The analysis report ofFIG. 18 illustrates results for undiluted samples known to be inhibitory(col. 2-6) and corresponding external matrix controls in col. 1 showinginhibition of the assays of col. 2-6. The analysis report of FIG. 18further illustrates results for dilutions of the samples at col. 8-12and corresponding external matrix controls in col. 7, showing relief ofinhibition of the assays with the exception of the assay correspondingto the sample of row D.

FIG. 19 illustrates results of an application of a trainedmachine-learning system (e.g., trained according to the exampletechnique of FIG. 10A) to the data sets of FIG. 18, in accordance withone aspect of this disclosure. Clusters 250, 252 of data sets determinedto be true negatives (i.e., corresponding to a biological assay in whichthe target nucleic acid is not present or not present at a thresholdlevel) is shown near the top of the chart. Clusters 254, 256 of datasets determined to be associated with biological assays that testednegative for the target nucleic acid due to matrix inhibition are shownnear the bottom of the chart.

FIG. 20 illustrates an example application of a trained machine-learningsystem to data sets of quality assurance (QA) laboratory negativecontrol runs, in accordance with one aspect of this disclosure. In someexample approach, the machine-learning system applied is trainedaccording to the approaches discussed for FIGS. 10A and 10B above. Ascan be seen in FIG. 20, the trained machine-learning algorithmclassified each of the QA lab-negative control runs as being a truenegative at a relatively high probability.

FIG. 21 illustrates an example of a collection of data sets, whereineach data set is represented by a curve representing light intensityover time during one or more nucleic acid amplification cycles, eachcurve corresponding to a sample known to include an inhibitory matrixresulting in a false negative result. FIG. 22 illustrates an exampleapplication of a trained machine-learning system (e.g., trainedaccording to the example technique of FIG. 10C) to the data sets of FIG.21, in accordance with one aspect of this disclosure. As illustrated inFIG. 22, the trained machine-learning system validated most of theinhibited samples at a relatively high probability of not being a truenegative result (i.e., of being a false negative result and, therefore,corresponding to a biological assay that tested negative for the targetnucleic acid due to matrix inhibition).

FIGS. 23-25 illustrate application of the machine learning system todata sets from samples containing 6, 7 dihydroxycoumarin, in accordancewith one aspect of this disclosure. FIG. 23, for instance, illustratesan analysis report produced by an example pathogen detection system fora collection of data sets representing nucleic acid amplification cyclesperformed on samples containing 6, 7 dihydroxycoumarin. As shown in thereport of FIG. 23, none of the samples containing 6,7 dihidroxycoumarinwere identified as including an inhibitory matrix resulting in a falsenegative result based on results of corresponding nucleic acidamplification and detection assays.

FIG. 24 illustrates the collection of data sets of FIG. 23, wherein eachdata set includes a time-series set of measurements of one of the curvesfor samples shown in FIG. 23, each curve representing light intensityover time during one or more nucleic acid amplification cycles, eachcurve corresponding to one of the samples containing 6, 7dihydroxycoumarin. FIG. 25 illustrates the results of an application ofa trained machine-learning system (e.g., trained according to theexample technique of FIG. 10C) to the data set of FIG. 23, in accordancewith one aspect of this disclosure. As illustrated in FIG. 25, thetrained machine-learning algorithm classified most of the samplescontaining 6, 7 dihydroxycoumarin as being a true negative result (i.e.,corresponding to a biological assay in which the target nucleic acid isnot present or not present at a threshold level) at a relatively highprobability.

As illustrated generally in FIGS. 14-25, the systems and methodsdescribed herein for training and using machine-learning systems mayenable such trained machine-learning systems to distinguish betweenbiological assays that truly test negative for a target nucleic acid andbiological assays that falsely test negative for the target nucleic aciddue to matrix inhibition with generally high levels of accuracy,precision, and recall. Thus, the trained machine-learning systemsdescribed herein may help reduce or eliminate the need for the use ofinternal or external controls with nucleic acid amplification anddetection methods such as LAMP. Moreover, the methods for training andusing machine-learning systems described herein may utilize backgroundor baseline portions of a reporter (e.g., light) signal in molecularmethods for amplifying and detecting a target nucleic acid. In methodsother than those described herein, the background or baseline signal isoften subtracted from the total reporter signal processing during a DNAamplification technique and little information is obtained from it.Thus, systems and methods described herein may utilize an otherwiseunused inherent background or baseline portion of a reporter signal toimprove methods of detecting whether a biological assay tested negativefor the target nucleic acid due to matrix inhibition in a correspondingsample tested for a target nucleic acid in methods for pathogendetection and/or quantification. Ultimately, such systems and methodsmay reduce time, cost, and/or complexity of biological assays used forpathogen detection and/or quantification and may help protect consumerhealth by limiting the release of potentially pathogen-contaminatedproducts.

Various examples have been described. These and other examples arewithin the scope of the following claims.

1. A system for detecting inhibition of a biological assay, comprising:a detection device configured to amplify and detect a target nucleicacid associated with a target organism during the biological assay, thedetection device comprising: a reaction chamber configured to receive asample comprising a matrix and a quantity of the target nucleic acid andto amplify the target nucleic acid within the sample over a nucleic acidamplification cycle; a detector, the detector configured to capture,during the nucleic acid amplification cycle, measurements representativeof a quantity of the target nucleic acid present in the sample and tostore the measurements in a data set; and a machine learning systemconfigured to receive the data set, wherein the machine-learning systemincludes processing circuitry trained to detect biological assaysinhibited due to matrix inhibition.
 2. The system of claim 1, whereinthe detector measures at least one of bioluminescence, fluorescence,absorbance, transmittance, or reflectance.
 3. The system of claim 1,wherein the detector is configured to detect the nucleic acid from atleast one of live cells, injured cells, stressed cells, or viable butnon-culturable cells.
 4. The system of claim 1, wherein the reactionchamber is configured to perform an amplification technique comprisingone or more of LAMP, PCR, nucleic acid sequence-based amplification, ortranscription-mediated amplification.
 5. The system of claim 1, whereinthe sample is selected from one of a food, a feed, water or a rawmaterial.
 6. The system of claim 1, wherein the sample is anenvironmental sample from an environment in which at least one of afood, a feed, water or a raw material is harvested, processed, packaged,or used.
 7. The system of claim 1, wherein the machine-learning systemis further trained to estimate a quantity of the target organism presentin the sample based on measurements present in: a first data subset, thefirst data subset including the measurements taken prior to a timeT_(max), wherein the time T_(max) corresponds to a time in the nucleicacid amplification cycle when the measurements reach a maximumamplitude; a second data subset, the second data subset including themeasurements taken after the first point in time but before a secondpoint in time in the nucleic acid amplification cycle, the second pointin time occurring after T_(max); and a third data subset, the third datasubset including the measurements taken after the second point in timein the nucleic acid amplification cycle.
 8. The system of claim 1,wherein the target organisms are microorganisms of one or moreSalmonella species, one or more Listeria species, one or moreCampylobacter species, one or more Cronobacter species, one or more E.coli strains, one or more Vibrio species, one or more Shigella species,one or more Legionella species, one or more B. cereus strains, or one ormore S. aureus strains, one or more types of viruses, or one or moregenetically modified organisms.
 9. The system of claim 1, wherein thereaction chamber is further configured to amplify the target nucleicacid in the sample over a plurality of nucleic acid amplificationcycles, and wherein the detector is further configured to capture themeasurements across the plurality of nucleic acid amplification cycles.10. The system of claim 1, wherein the machine learning system is basedon a regression model.
 11. The system of claim 1, where the reactionchamber is further configured to receive a module, wherein the moduleincludes: a first plurality of reaction vessels, each vessel of thefirst plurality of reaction vessels containing a quantity of a lysisbuffer solution; and a second plurality of reaction vessels, each vesselof the second plurality of reaction vessels containing quantities of oneor more reagents configured for use in a nucleic acid amplificationreaction.
 12. A method, comprising: receiving a plurality of data sets,wherein each data set is associated with a biological assay, each dataset including measurements, performed on the associated biological assayby a nucleic acid amplification device of a specified type and collectedover at least a portion of a nucleic acid amplification cycle, of atarget nucleic acid detected within the associated biological assay,wherein the target nucleic acid is associated with a target organism;labeling, as false negative data sets, those data sets from theplurality of data sets that are associated with biological assays thattested negative for the target nucleic acid due to matrix inhibition andthat would have tested positive had matrix inhibition not been present;labeling, as true negative data sets, those data sets from the pluralityof data sets that are associated with biological assays that correctlytested negative for the target nucleic acid; and training amachine-learning system with the true negative and false negative datasets to detect biological assays that tested negative for the targetnucleic acid due to matrix inhibition.
 13. The method of claim 12,wherein the measurements are time-series measurements of light intensitycollected over at least a portion of the nucleic acid amplificationcycle.
 14. The method of claim 13, further comprising training themachine-learning system to estimate a quantity of the target organismpresent in the sample based on measurements present in: a first datasubset, the first data subset including the measurements taken prior toa time T_(max), wherein the time T_(max) corresponds to a time in thenucleic acid amplification cycle when the measurements reach a maximumamplitude; a second data subset, the second data subset including themeasurements taken after the first point in time but before a secondpoint in time in the nucleic acid amplification cycle, the second pointin time occurring after T_(max); and a third data subset, the third datasubset including the measurements taken after the second point in timein the nucleic acid amplification cycle.
 15. The method of claim 12,wherein the measurements are time-series measurements of light intensitycollected over the entirety of the nucleic acid amplification cycle. 16.The method of claim 12, wherein the measurements include measurementscollected but not normally included in test results presented by thenucleic acid amplification device.
 17. The method of claim 12, whereinthe nucleic acid amplification device performs an amplificationtechnique comprising one or more of LAMP, PCR, nicking enzymeamplification reaction (NEAR), helicase-dependent amplification (HDA),nucleic acid sequence-based amplification (NASBA), ortranscription-mediated amplification (TMA).
 18. The method of claim 12,wherein the biological assays are from a matrix inoculated with two ormore levels of organisms and wherein labeling each data set with anestimate of the quantity of the target organism includes setting thequantity as a function of the level of inoculation.
 19. The method ofclaim 12, wherein the biological assays are from a plurality of matrixtypes and wherein training a machine learning system includes trainingthe machine learning model to distinguish between matrix types.
 20. Anon-transitory computer-readable medium storing instructions that, whenexecuted by processing circuitry, cause the processing circuitry to:establish a machine-learning system trained to detect matrix-inhibitedbiological assays; receive a data set generated by amplifying anddetecting a target nucleic acid associated with a target organism in asample comprising a matrix and a quantity of the target nucleic acidover a nucleic acid amplification cycle, the data set includingmeasurements representative of the quantity of the target nucleic acidpresent in the sample; determine, by applying the machine-learningsystem to the data set, whether the data set is from a matrix-inhibitedsample; and label the data set accordingly.
 21. (canceled)